Table of Transcription and Transliteration vowel system used.

1.1. Historical and Linguistic Introduction

Historical Framework

In order to comprehend the Tunisian Arabic heritage of human contact, small fragments of both the linguistic mosaic that Tunisian Arabic constitutes today and the Tunisian linguistic ideology, it is necessary to trace back the history of this country, and consequently of its people. We must start by geographically placing Tunisia as a North African country halfway between the western and eastern North African countries. Its borders to the North-East coincide with the coast on the Mediterranean Sea, which is a very important factor in terms of the history of human and linguistic contacts of this country. To the southeast, it borders Libya, while its entire western and southwestern borders are shared with Algeria. Because of its location at the center of the Mediterranean, Tunisia has always been a land of passage, cultural exchanges and a basin of ancient civilisations, such as Carthage (Marçais, 1950).

Carthage was founded in the ninth century BC by the Phoenicians, a population that has long dominated the Mediterranean. The homeland of the Phoenicians corresponds to the current territory of Lebanon, where their ancestors have lived since the third century BC. Carthage rises on the shores of today's Gulf of Tunis. For the rising empire, its geographical position was a strategic crossroads, as it was the obligatory route for all the commercial ships travelling along the route between East and West.

Upon arriving in Tunisia, the Phoenicians met the indigenous population, who spoke Lybian-Berber, which is still spoken by a good number of North Africa inhabitants, and of which many proper names of places and people are the only remains. 2 It is unclear whether the Berbers were the only 'pre-Tunisian' population, and the issue is fascinating, but still obscure (Picard, 1950). 4 The Carthaginian Empire was one of the greatest empires of antiquity, and in its brightest moments, the port city of Carthage was the symbol of its magnificence for all the Mediterranean peoples. For about a millennium, the Carthaginians dominated the Mediterranean coasts, and were known and respected by other peoples for the art of navigation and their aptitude for commerce. The greatest enemy of the Carthaginian Empire was the Roman Empire, and it is precisely because of Romans that not much remains from this great civilisation.

Travelling is a fundamental concept for me, perhaps the motivation behind my passion for languages, for knowledge, for research. In every travel I have done, I have seen places and met people who have taught me something and enriched me inwardly.

This journey specifically began in Rome during my university studies, when a spark of interest and curiosity towards computational linguistics was deposited in me. This spark did not fade during the two years between my Master's degree and the start of my PhD, which saw me in Tunisia. Indeed, the desire to confront myself with the Arabic language and culture, in an everyday dimension, was too strong to postpone any longer.

Tunisia welcomed me like a mother, strict but fair. It removed a veil from my eyes, one of many, and confronted me with the fragility of my certainties. Tunisia prepared me to begin this research. Back in Rome, I began this unique experience of a PhD research, which soon became a PhD in co-tutorship with the University of Grenoble Alpes, an increasingly articulated voyage.

I would like to take this opportunity to thank the laboratories that welcomed me at the Université Grenoble Alpes: Laboratoire de Linguistique et Didactique des Langues Etrangères et Maternelles (LIDILEM) and the Laboratoire d'Informatique de Grenoble (LIG), but also the department Istituto Italiano di Studi Orientali (ISO) that accepted my candidature for the PhD at Sapienza, Università di Roma, and the Università Italo-Francese of Turin for the financial support provided during the co-tutorship.

Sincere thanks go especially to the professors who directed the research work: Giuliano Mion and Olivier Kraif, as supervisors, and Marco Dinarelli with Giuliano Lancioni, as co-supervisors. I thank them for advising me, listening to me and supporting me at all stages of this exciting path. Without them, this work would not have been possible.

I also thank the professors who have been part of my Comité de Suivi Individuel : Salam Diab-Duranton and Laura Mori, who have monitored my research over the years. I thank the professors who patiently revised my thesis, Simone Ciccolone and Luca v D'Anna, whose advice and corrections were invaluable. I thank the professors who agreed to be part of the jury for my dissertation in Rome: Houda Bouamor, Francesco Grande, Carmela Perta and Agnès Tutin. Their observations and suggestions were very helpful in defining my future steps in the research.

I would also like to thank Professor Mario Casari, coordinator of the PhD curriculum in Arabic, Iranian and Islamic Studies at the Sapienza University of Rome, for the dedication and support he has provided to me and my colleagues during these years.

Finally, I thank my colleagues and friends with whom I shared this journey, who contributed to the construction of a treasured personal memory.

Transcription and Transliteration

Table

With regard to the system of transcription and transliteration chosen for this work, in the hopes that this work will reach a wide and varied readership, we have opted for an IPA-oriented transcription system. Moreover, when dealing with a writing system, we could not take for granted the phonetic realisation intended behind the graphemes that composed the texts we used. As pointed out by Durand (2012) and [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF], the phonological representation for Tunisian flattens the phonetic richness of this variety, which presents, for example, a vowel system of six phonemes, i.e. three short and three long, as shown in the table below. However, the phonetic realisation of these phonemes varies depending on many factors, such as the diatopic, diastratic, the phonemic context, etc. Therefore, a phonological rendering through the graphemes is generally adopted, which will undoubtedly be reductive, not only with regards to the vocalic system. Therefore, for our work, an exact phonetic reconstruction would be impossible, which is why we opted for a mixed IPA-oriented transliteration system oriented toward the Tunisian koiné, which, as we will see below, represents the 'national' colloquial variety, even if in dialogue with the local varieties. For the same reason, we have reported short vowels as schwa, /@/, with the exception of the short vowel [u]. In the tables below, we report the International Phonetic Alphabet symbols associated with the phonetic realisation of the grapheme following the Tunisian koiné realisation.

Next to this, we report the employed transcription system (between // in the text), the grapheme name and its encoding in Arabic script. Arabizi texts in the examples will be in italics.

Regarding [g]* in the table below, it could be considered to be a qāf allophone, but it is also a loan phoneme from the Romance languages.

xiii IPA Used Transcription Grapheme Name Graphemes [b] b bāP

[t] t tāP

[T] T TāP

[Ã][Z] ǧ, ž ǧīm [è] è èāP [X] h ˇh ˇāP [d] d dāl [D] D Dāl [r] r rāP [z] z zāy [s] s sīn [S] š šīn [s Q ] s ˙s ˙ād [d Q ] d ˙d ˙ād [t Q ] t ˙t ˙āP [D Q ] D ˙D ˙āP [Q] Q Qayn [K] G Gayn [f] f fāP [q]
q qāf

[g]* g gāf

[k] k kāf [l] l lām [m] m mīm [n] n nūn [h] h hāP [P] P hamza

Introduction

The object of this work is Tunisian Arabic, and more specifically its encoding in Arabizi. Arabizi is an orthographic encoding, which arose spontaneously in a diamesically influenced context, namely digital environments. The encoding we refer to uses the Latin alphabet, as well as some numbers, for Tunisian Arabic phonemes with no correspondence in the Latin script.

The development of the Internet, along with that of technology, transformed written communication, leading to the rise of its proper mode: electronic written communication. This new form of communication has continued to spread widely since the mid-1990s. Technology in general has had a major impact on our everyday practices, the way we read the news, our work environment, the way we socialise and how we express our identity. Until a few years ago, many introductory texts, when referring to the so-called Arabic dialect, henceforth dārža, i.e. the Tunisian autoglottonym, used to describe it as a primarily oral variety, being almost completely without a written tradition, with just a few rare exceptions. In terms of Tunisian Arabic, there are in fact some texts written in dārža. However, it has already been at least twenty years since Tunisian native speakers started writing on social media, every day, in dārža.

This marked a major switch in language practices, which has been so significant that it might signal a break from the traditional definition of Tunisian Arabic as being 'predominantly oral'. Moreover, this is not the only manifestation of the writing in dārža.

Reference can be made to comics, the practice of writing graffiti on walls, advertising panels, blogs, forums, and in general to Computer-Mediated Communication (CMC), which is the main means of expression for the so-called 'digital natives' and their wish to communicate by writing in dārža.

As such, it can be considered to be a very powerful identity marker. Therefore, we need to analyse how the Tunisian digital generation spontaneously organised itself to communicate in its own dārža. Certainly, in the age of the Internet and CMC, writing and literacy rapidly takes on new forms and functions with a number of consequences for language and culture, as well as for economics and politics. Indeed, another example of how non-state agents have made use of new communication technologies with far-reaching consequences for both social and political events was the so-called Arab Springs of 2011. Regarding the vernacular writing practice, Auer (1997) relates it to the koineisation process. The latter results in the adoption of a national urban koiné, which becomes prevalent in public spaces, while the vernacular, which is specific to the place of origin, is confined to domestic interactions.

On the other hand, Auer (1997) defines this process as destandardisation explaining that it consists of different regional vernacular features converging and simultaneous standard variety decreasing of the standard status flowing into larger-scale regional koinai. When taking the wide diffusion of the practice of writing on social networks into consideration, as such a spontaneous and informal mode of communication, this allows us to imagine that an analysis of data gathered from the CMC context can reveal what kind of spontaneous linguistic dynamics of this type are currently taking place in Tunisia. Arabizi seems to be the most representative object of study in this context of computer-mediated writing. In fact, Androutsopoulos (2012) has already addressed a similar phenomenon, namely Greeklish, through both approaches, i.e. the autonomous one and the ideological one, specifying that an autonomous approach sees orthography as a neutral technology for the representation of spoken language, in contrast with the so-called ideological approach that views orthography as a set of social practices in specific social and cultural contexts.

Considering that technology has a large effect on our use of language and the way we interact with each other, the research that has focused on this issue ranges from analyses of discourses with approaches from ranging from communication sciences to psychology and sociology, to the methodological way of analysing same, which can be comparative with other modes of discourse or can focus on specific linguistic phenomena.

We focus on the Arabizi system in use for writing Tunisian Arabic in order to observe the Tunisian Arabizi diachronic evolution on the one hand, but to also bear witness to the distribution of certain textual features depending on the more or less informal context. To this end, we consider a description of the Tunisian multilingual context to be an invaluable jumping-off point, in terms of diglossia and bilingualism.

Therefore, with the purpose of introducing Tunisian Arabic, we can delve deep into the multilingual and diglossic context that characterises it.

Specifically, our work begins by tracing back some of the historical linguistic milestones of Tunisian, which will constitute some of the tools necessary to depict the current linguistic complexity. We begin with the historical survey to consciously address questions regarding the status of Tunisian Arabic. We will then move into deeper linguistic issues, and we will see how Tunisian Arabic is classified in terms of traditional dialectological descriptions. We will also observe how this Tunisian multilingual society has been historically and modernly described by scholars, and what role Tunisian Arabic takes in society compared to that of Modern Standard Arabic (MSA) and French, in an attempt to describe all the nuances that exist between the different shades of the Tunisian linguistic ideology. We will finally examine the evolution of the Tunisian socio-linguistic landscape, which is a fairly common component part when studying the history of languages, namely the emergence of urban varieties with the status of koiné. As mentioned previously, this process is particularly relevant to the analysis of Tunisian CMC. In fact, it is in particular the koiné of Tunis that has had consistent diffusion through the early means of diffusion of oral texts, such as radio and television.

In fact, we asked ourselves several questions at the beginning of the project. The first concerns the kind of language we should expect to find in these digital contexts. [START_REF] Hary | The importance of the language continuum in Arabic multiglossia[END_REF] defines multiglossia as the linguistic situation in which different varieties coexist side by side in a language community, and that each one is used in different circumstances and has different functions. Thinking back to the principles of Hary's functions of communication, it comes to mind that a certain variations will be found depending on the contexts of communication and therefore on the channels. In our case, the channels are forums, blogs or platforms for simultaneous communication, such as Facebook.

At the same time, a series of questions arise regarding the hypothesis of a continuum of (in)formality, given the more or less spontaneous context of the communication. One of the objectives of the preliminary analyses set out in chapter 4 is in fact to verify whether there is a basis for assuming the existence of this continuum between textual genres. In this case in which the continuum would go from a formal to an informal textual genre pole, the more formal one could be represented by genres such blogs, which are often narrative and do not involve simultaneous communication. The more informal could be those marked by communication exchanges, which usually occur in sync, where real networks of social connection are created or reproduced, such as Facebook texts. An intermediate genre could be represented by those typical of certain platforms with characteristics shared between the two poles, such as forums. In fact, forums allow us to find real networks of social connection, despite not being in sync.

Another issue that we will explore through the analyses is the quasi-oral nature of this encoding system. Indeed, considering Arabizi as a non-standardised system, it is possible to assume that it allows users to be more independent from strong writing traditions, such as the one related to Arabic writing. This is one of the hypotheses about Arabizi that we will address. Furthermore, Arabizi presents a huge variety in its orthography, with many different possibilities for encoding one word. Our aim is to investigate precisely whether this freedom of graphic realisation is constrained by the fact that Arabizi belongs to a specific writing context. Before the invasion of Facebook, when one wrote in Tunisian, one did so in Arabic characters and according to some implicit but widely shared rules of 'correct encoding'. We would also like to consider the possibility that these rules are based on a transfer that took place through the Arabic alphabet when employed in Tunisian Arabic writing practices. The alternative is that Arabizi allows the user to get rid of some rules which are normally 'imposed' by Arabic spelling, and all that is needs to be considered is 'correct encoding' in Tunisian Arabic.

This possibility includes the concept of Arabizi being influenced by French orthography, being a Latin character encoding, and facilitating the use of French vocabulary during code-mixing.

A further preliminary analysis that we were interested in was related to the process of the settling of Arabizi writing practices, which we have defined as being a spontaneous and non-standardised system. However, each Arabizi national variety should have been subjected to a process of spontaneous settling since its inception. This is true also for Tunisian Arabizi.

In order to be able to carry out our analyses on this writing system, we must first describe the tools that enabled us to conduct our research. Indeed, our present work comes with the objective of building a set of tools to support the analysis of this language considering the lack of resources which can be used to conduct research on Tunisian Arabizi. A tool we built is called the Tunisian Arabish Corpus (TArC). It is a corpus that collects texts from different CMC contexts, such as forums, blogs and social networks, amounting to 43,327 words. TArC has been provided with different levels of linguistic annotation in order to support different types of analysis. As for the name we chose for the corpus, the Arabizi encoding is known by many different names, depending on which Arabic country you are in. The name 'Arabish' was used, rather than 'Arabizi', because it is easily understandable even among non-Arabic speakers.

Nevertheless, the term Arabish also allows for a direct reference to similar phenomena.

In fact, many linguistic systems, which are formally encoded through Roman script, use the Latin alphabet for informal exchange in CMC contexts, among them are the previously mentioned Greeklish (which is blend of 'Greek' and 'English') or Pinglish (a merge between 'Farsi' or 'Persian' and 'English'). However, the term 'Arabizi' is now more widespread than 'Arabish', even if it is not so transparent, as it is the portmanteau of 'Arabic' and 'English' (/Pinglīzī/), which is the Arabic term denoting the English language. For this reason, we decided to refer to this encoding, throughout this body of work, using the term Arabizi.

In order to perform the mentioned analyses, a corpus of a certain size was required, so that it could represent the different textual varieties, but could also support analyses in diachrony and diatopy, as well as the general observation of diastratic variations.

We will explore what resources are available for the study of Tunisian and its encoding in Arabizi, but we had already anticipated the fact that, given the breadth of the issues we were interested in observing, the best solution for us was to create a corpus from scratch. We therefore decided to opt for a hybrid methodology that would allow us to build a corpus of Tunisian Arabizi of a sufficient size to contain a certain degree of variability and that would have levels of linguistic information with which to facilitate our analyses.

For many reasons, which are mentioned below, the adjective that best describes our approach to this work is 'hybrid'. First of all, the project is located in a space of intersection between different research fields: Arabic dialectology, corpus linguistics, and Natural Language Processing (NLP). Second, the texts collected in our corpus are encoded in a script that has hybrid characteristics of orality while also being a writing system. Once we opted for this hybrid method, we also realised that this would allow us to expand the pool of utility of the tool itself. In fact, we could integrate the construction procedure with semi-automatic corpora construction procedures. This choice has meant that the corpus can then be used in NLP research. Finally, our motivations for building a corpus from scratch coincide with the utility we envisioned upon its release, both from a linguistic and an NLP perspective.

After defining the motivations that led us to build the Tunisian Arabish Corpus (TArC) from scratch and the methodology we adopted based on the state-of-the-art we will move on to describing the specific operations that led us, step by step, to the TArC realisation. The steps we will describe follow the path from the beginning, starting with the collection of data and then moving on to decisions regarding the selection and collection of metadata. We will also describe the stages of semi-automatic annotation of the corpus into its component layers: i.e. the transliteration of Arabizi words into Arabic script, the tokenisation of words in morphemes, Part-of-Speech tagging and lemmatisation. We will then review the steps and the experiments that led us to identify the best strategies with which to achieve our corpus building goals. Finally, we will describe the Multi-Task Sequence Prediction architecture that was built to produce the different annotation layers that make up TArC from the Arabizi texts. In the end, we will also describe the TArC structure, along with information about the amount of data and metadata.

Finally, before delving into the details of this research, we would like to make an anticipation in order to immediately highlight the boundaries of the research here presented. These boundaries therefore exist due to an a priori choice regarding the positioning of this study in the field. This research, in fact, aspiring to be placed in an interstitial point between different research fields, with the aim of combining tools and goals common to them, runs the risk of being perceived as atypical. In a way, this would not even be a completely incorrect perception, in the sense that placing this study in only one of the disciplines from which it draws would not be feasible. However, the a priori choice was precisely to position itself in an intermediate place between them, but in full communication with them. Here, however, factors other than those concerning the intentions and boundaries of the research intervene, namely the limits of the data.

An ambitious challenge has certainly been to maintain a certain balance in the contributions that each research area has made to this study, without letting itself be carried away by the positivist enthusiasm that the use of certain tools, for better or worse, implies. This challenge encounters its own limitations in the analysis section, firstly because these are preliminary analyses, and secondly because the type of data collected in the TArC is indeed data that falls within the sphere of Arabic dialectology (precisely one of the three fields of study involved), since it deals with texts in Tunisian Arabic, but it is not the prototypical data observed in dialectological analyses. However, we wanted to try to adapt some strategies of dialectological investigation to this type of data, precisely in order to delimit the range of linguistic research that can be carried out on this spontaneous writing system. In particular, we are dealing with analyses on the process of koineisation. This choice is motivated by the intention to orient the reader towards the possible interactions that a scholar working on dārža could have with this type of data, such as the use of the TArC as a collection of data that can support, at a quantitative level, certain hypotheses or qualitative analyses. Given the nature of the data that the TArC contains, it is naturally not always possible to exploit its contents. As will be explained in the analyses chapter and in the conclusions, some hypotheses for the interpretation of the data highlighted will be proposed, these hypotheses must therefore be taken in this perspective of preliminary observation aimed at marking not so much a point of arrival as a starting point for future research.

This work features four chapters and an appendix, where it is possible to observe some of the data automatically extracted from TArC during our analyses. The analyses are reproducible, as each tool which was produced and employed has been made available on dedicated websites. These tools are the Tunisian Arabish Corpus (TArC), the architecture designed for the TArC multi-level annotation, and the query scripts conceived for our analyses.1

Regarding the organisation of the topics in the four chapters, chapter 1 is dedicated to the historical and linguistic introduction of Tunisian Arabic (section 1.1). It will explore the issues inherent to the processes of orthographic standardisation in order to reach the description of spontaneous varieties which have arisen (section 1.2). Finally, the same chapter will also introduce the situation of the Tunisian CMC and the issue of digraphia, concluding with an introductory description of the main characteristics of the Tunisian Arabizi (section 1.3). Chapter 2 deals with the methodology adopted for addressing CMC corpus building using deep learning techniques (section 2.1). It will describe the corpus linguistic standards which have been our benchmark in making decisions about the structure of the corpus (section 2.3). The same chapter will also introduce some fundamental concepts in order to better appreciate the deep learning techniques adopted in the corpus construction phases (section 2.4). It will conclude with the state-of-the-art, in order to expose the path of Tunisian Arabic research in the Natural Language Processing (NLP) field. The state-of-the-art section also aims to highlight the lack of available tools for Tunisian Arabic processing (section 2.5).

Chapter 3 describes our journey, all the way from data collection (section 3.1) to the corpus building phases. There are two main parts to this, an earlier one in which we started annotating the corpus (section 3.2), and a second one in which we completed it using the multi-task system which was built for annotation purposes (section 3.3).

Finally, chapter 4 will describe the query tools employed for our analyses (section 4.1)

and the observations we made on the TArC data. Our analyses aim to delineate the nature of the corpus itself and investigate the linguistic reality of Tunisian Arabizi.

As briefly hinted at above, the analyses will follow three paths: the quasi-orality path (section 4.2), the research on Arabizi spontaneous settling trends (section 4.3) and the quest for evidence of the existence of a continuum of formality (section 4.4).

Chapter 1

Tunisian Arabic Ideology

Historical and Linguistic Introduction

In its most elementary sense, 'linguistic ideology' is what determines how speakers of a given language interpret their language, and consequently how they use it. Linguistic ideology is a matter of beliefs and mental images involving societal concepts and their linguistic representations.1 Linguistic ideology moves and marks the identity of the speaker, in association or disassociation with certain social groups. In general, it is not easy to describe the linguistic ideology of any culture. Moreover, Tunisian presents a particularly nuanced linguistic ideology, because of its historical, geographical and socio-political features.

In this section we will therefore retrace some of the historical linguistic stages of Tunisian, which will constitute a part of the toolkit necessary to portray the current linguistic complexity thereof. This begins with the historical survey in section 1.1.1 in order to consciously approach the question regarding Tunisian's status as a so-called Arabic dialect. We will then move into deeper linguistic matters, and in section 1.1.2, we will instead see how Tunisian Arabic is ranked in terms of traditional dialectological descriptions. Throughout section 1.1.3, we will see how the Tunisian multilingual society has historically and modernly been described by scholars, and what role Tunisian Arabic assumes in society compared to that of Standard Arabic and French, in an effort to describe all the gradations that exist between the different shades of Tunisian linguistic ideology. In section 1.1.4, we will finally examine the decline in the Tunisian socio-linguistic landscape, which is a fairly common process in the history of languages, namely the emergence of urban varieties with the status of koiné. This process is particularly relevant to the Tunisian Computer-Mediated Communication analysis, as we will see in later sections (namely 1.3.1).

Historical and Linguistic Introduction

The two had quietly coexisted for centuries, until the outbreak of the Punic Wars (264 BC). After Carthage's destruction (146 BC), Tunisia became a Roman province called Africa. It was only in 647 AD that the Arabic language made its first appearance in North Africa, adding itself to the already multicultural and multilingual landscape of Tunisia at the time. The Islamic Empire took half a century to conquer Byzantine Carthage (698 AD). To be exact, the first wave of diffusion of the Arabic language was conveyed by military campaigns that laid the foundations for Arabisation, encouraging the rise of the first urban centers, such as al-Qayrawān (670 AD). In these cities, people gradually began to speak the so-called Pre-Hilali Arabic (a theme we will return to in detail in section 1.1.2), but outside the urban centers, people still spoke Berber [START_REF] Mion | La lingua araba. Nuova edizione[END_REF][START_REF] Mion | Pré-hilalien, hilalien, zones de transition.nrelire quelques classiques aujourd'hui[END_REF]. These troops settled in what were the great Greek-Roman-Carthaginian cities, also bringing with them the language of religion.

Between 1050 and 1052 AD the Bedouin troops of the Syrians Banū Hilāl and the Arabian Banū Sulaym invaded Tunisia again, in order to quell the Berber revolts against the caliphate's control once and for all. 5 From that moment on, Arabic became the predominant language in Tunisia, relegating all earlier language elements (Punic, Latin and Greek) to substrata and adstratum (Berber). 6 The initial situation of Arabic-Berber bilingualism was soon replaced by the overwhelming predominance of Arabic over Berber. In fact, from that moment onward, the Arabisation of Tunisia underwent a substantial acceleration, penetrating the Tunisian countryside and through the almost total conversion of the Berbers to Islam. Today, the Berber language, namely Tamazight, is considered endangered, and survives only in some areas of Tunisia, particularly in Mat . māt . a and Djerba (Ž@rba) [START_REF] Daoud | The language situation in tunisia[END_REF][START_REF] Baccouche | L'emprunt en arabe moderne[END_REF]Marçais, 1950;[START_REF] Camps | Comment la berbérie est devenue le maghreb arabe[END_REF][START_REF] Ritt-Benmimoun | The Tunsian hilāl and sulaym dialects. a preliminary comparative study[END_REF]Taine-Cheikh, 2017). 7 Although we will explore these issues in more detail in the following sections, we can already note the 5 A third group was the MaQqil from Yemen, as we will see in section 1.1.2. Here we refer to the traditional classification while being aware that this narrative has been subject to recent revisions. In fact, as briefly mentioned in the next section, [START_REF] Benkato | From medieval tribes to modern dialects: On the afterlives of colonial knowledge in arabic dialectology[END_REF] asserts that this classification is the product of a colonial view from which modern dialectology studies should break free. However, we believe it is useful for the purposes of this study to maintain the use of these traditional categories prevalent in modern dialectology textbooks.

6 A substratum is a language that has lost its power or prestige to another that has succeeded it, i.e., a superstratum. Both substratum and superstratum languages influence each other. An adstratum, on the other hand, is a language that is in contact with another language in a neighbouring population without having superior or inferior prestige.

7 According to [START_REF] Daoud | The language situation in tunisia[END_REF], the percentage of native speakers is less than 0.5% in Djerba and in some peripheral villages of the governorates of Mednīn and Tat . āwīn. For further information on the Djerba Berber variety, see [START_REF] Brugnatelli | Il berbero di jerba: rapporto preliminare[END_REF].

coexistence of three 'kinds of Arabic' in Tunisia as the main linguistic strata: pre-Hilali Arabic, basically coinciding with the large urban centers, the Hilali Arabic which instead corresponds more closely to the Bedouin and somewhat rural varieties. In a diglossic continuum 8 with these so-called Arabic dialects (henceforth Neo-Arabic varieties, namely, the last strata of the Modern Arabic dialects [START_REF] Blau | Classical Arabic, middle Arabic, middle Arabic literary standard, neoarabic, judaeo-arabic and related terms[END_REF])), we can find the Standard and Classical Arabic, crossing the whole country as an acrolect. 9

As the last historical stages of this brief excursus, we must consider the contact that Tunisia had with European civilisation, starting with Spain, following the exodus of the Arab-Berber Moors from Spain as a result of the Spanish Reconquista. In the following centuries, Spaniards and Turks vied for control over trade in the Mediterranean. In the 16th century, Tunisia was annexed to the Ottoman Empire and remained under Turkish domination until the second half of the 19th century, until Italy and France began to compete for supremacy in the North African region in the 20th century. One of the few testimonies of the contact woven into the shared history of these Mediterranean cultures is the Lingua Franca, a pidgin language of essentially Latin matrix [START_REF] Bannour | Brève mise au point sur la lingua franca en méditerrannée[END_REF]. There is no descriptive model for the linguistic erosion of the languages that merged to originate this language, but we know that the dominant languages were French, Italian and Spanish. 10 The Lingua Franca language was widely spoken, and it was through this language that trade relations (among other things) were brokered with the Mediterranean civilisations. For instance, Italy has a long history of linguistic and human contact with Tunisia because of its maritime activity, which was the basis of both the Italian and Tunisian economies. In addition, Tunis has been a safe harbor for numerous persecuted Jews throughout history. As [START_REF] Cohen | Le parler arabe des juifs de Tunis: textes et documents linguistiques et ethnographiques[END_REF] reports, there were Sicilian Jews, Jews who were persecuted and fled the Iberian Peninsula, Neapolitans, and more generally Italians, particularly from Ancona and Livorno. The latter were known as Grana, a prestigious family, whose descendants still live in the Medina of Tunis (Cohen, 1964, 4-6). 11 This large community of Jews has coexisted with 8 For the definition of diglossia, see 1.1. 3. 9 The most prestigious dialect or variety of a particular language. 10 Instead of Italian, it is more correct to discuss Venetian, Genoese and Florentine, even though historical documents simply refer to Italian. We also know that in Maghreb, to the west of Algiers, the most prominent was Spanish, particularly in Morocco. Other contributions came from Provençal, Portuguese, Arabic and Turkish, as well as from Greek and Armenian. However, in Tunis, there are also Greek and Turkish components. It was a very recognisable language, with a basic morphology, mainly comprising infinitive verbs. For further information see [START_REF] Cifoletti | La lingua franca barbaresca[END_REF].

11 Grāna, pl. of Gūrni from Leghorn (Livorno), in opposition to the indigenous Jewish community which were simply referred to as twānsa, tunisian. The Grana family was also well known because of 1.1. Historical and Linguistic Introduction the Muslim community throughout history. Above all, starting in the 19th century, a large number of Italian laborers from the south, in particular from Sicily, poured into Tunisia. At the beginning of the 19th century, in fact, the number of Italians resident in Tunisia was at least 11,200 people. 12 In fact, Italy had great economic investments in Tunisia, to such an extent that an Italian company (Compagnia Rubattino) owned the rail line Tunis-Goletta. La Goletta, later Frenchified as La Goulette, or H . alq @l-Wādi in Tunisian, is a coastal town in Tunisia located 10 km away from the capital Tunis, and is the outer port of same. La Goulette was populated by Italians, and was thus nicknamed La Petite Sicilie. 13 As such, many Italian words are now integrated into Tunisian Neo-Arabic, particularly in the lexicon of construction, agriculture, shipping and the arts. However, within the same period, it was another European language that gained importance: the French language. With the collapse of the Ottoman Empire, French was imposed as a second language in light of Tunisia becoming a French protectorate in 1881. 14 At this point, Tunisia found itself living under bilingualism again.

The effects of this Frenchisation process have lasted until today, with Tunisia experiencing a very particular linguistic situation in which French has become so ingrained in Tunisian culture as to be perceived by some as a threat to the Arab-Muslim identity [START_REF] Daoud | The language situation in tunisia[END_REF][START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF]). This will be discussed in section 1.1.3. Recently, English has also been added into the mix as the language of technology and of the globalised world.

In concluding this section, we would like to quote the words of [START_REF] Mejri | Pluringuisme et diglossie en tunisie[END_REF], according to whom: the market bearing their name: sūq @l-Grāna, between Bāb Swīqa and H . afs . īya (Singer, 1984, 472).

12 Whereas the French settlement was as small as 700 and 7,000 British, mostly Maltese. By 1906, when Tunis had already been a French protectorate for 25 years, Italians still accounted for the majority of foreigners (81,000) vs. the numbers of French (34,000) and Maltese (10,300). 13 Just as an example of the cultural link between Italians and Tunisia, we report that La Goulette was the home place of Yolanda Greco, the mother of the Italian actress Claudia Cardinale ["kla:udja kardi"na:le], who was elected the most beautiful Italian woman in Tunis in the mid 50s.

14 With the Treaty of Bardo, which was concluded on 12 May 1881. This was followed by the Convention of Marsa of 8 June 1883, which gave France the right to intervene in Tunisia's domestic affairs [START_REF] Lewis | Divided Rule: Sovereignty and Empire in French Tunisia, 1881Ð1938[END_REF]).

1.1. Historical and Linguistic Introduction 'L'actuel dialecte n'est, en fait, que le fruit de différentes strates (substrats, adstrats et superstrats) : un beau syncrétisme qui traduit bien une évolution de la société tunisienne à travers les différentes époques historiques et qui reflète proportionnellement le poids des différentes cultures développées sur ce territoire.' 15After quickly describing Tunisia's multilingual historical landscape and before addressing issues related to Tunisia's modern linguistic configuration, in the next section we will examine the debate among linguists regarding the formation of Arabic dialects and the possible resulting classifications.

Neo-Arabic Classification

In the twentieth century, a number of scholars attempted to explain the presence of several linguistic features common to Neo-Arabic varieties with respect to Classical Arabic (or fus . èā). [START_REF] Ferguson | [END_REF], for example, proposed the theory of monogenesis, which sees a single point of origin for the contemporary proto-dialect originating from military camps in Iraq, through the mixing of various pre-Islamic dialects spoken by soldiers. Such a proto-dialect, according to the theory of monogenesis, thus coincides with a military koiné, in which common features developed. Ferguson lists fourteen features, the first of which, for example, is the disappearance of the dual form in verbs, pronouns, and adjectives. 16 The differences between Neo-Arabic varieties are then explained as the result of a later process of divergence, probably due to the influence of the indigenous languages of the various Arabised regions. Critics of the monogenesis theory have objected to this, stating that the similarities could also be explained as the product of a general trend, or as the result of a subsequent process of convergence that leveled off the dialects in the various areas. They point, for example, to the fact that, like the Neo-Arabic varieties, some languages which are unrelated to Arabic have also lost the dual form. The main problem with the theory of a general trend is that the explanatory power of such a principle is minimal, since the mere fact that similar phenomena occur in different languages does not provide us with an explanation of the causes behind them. [START_REF] Versteegh | Pidginization and creolization: The case of Arabic[END_REF] proposed the pidgin theory, which saw the Arabic of the early conquests first creolising the indigenous languages into a new linguistic form, and then later reversing the trend through subsequent internal diversification producing the Neo-Arabic variation [START_REF] Durand | Dialettologia araba[END_REF]. According to Corriente's model, on the other hand, fus . èā is itself the end point of an evolutionary path, which is shared by other varieties of Arabic [START_REF] Corriente | From old arabic to classical arabic through the pre-islamic koine: Some notes on the native grammarians'sources, attitudes and goals[END_REF]. It thus follows that Neo-Arabic varieties represent a heritage as old as that of Old Arabic. From this perspective, the ancestors of Neo-Arabic varieties did not arise after the waves of Arabisation, but rather existed simultaneously and contributed to the development of Standard Arabic. In the opinion of certain scholars, Classical Arabic is derived from a common ancestor in a proto-Arabic language that has yet to be reconstructed [START_REF] Embarki | Les dialectes arabes modernes: état et nouvelles perspectives pour la classification géo-sociologique[END_REF]. According to [START_REF] Cohen | Etudes de linguistique semitique et arabe (Studies of Semitic and Arabic Linguistics)[END_REF], Neo-Arabic varieties are the result of a progressive convergence of 16 For a definition of koiné, see 1.1.4. 16 1.1. Historical and Linguistic Introduction dialects spoken in Arab armies, composed of individuals belonging to different tribes. It is his opinion that the differences between the pre-Islamic dialects were leveled a priori, while the dialects in the conquered territories evolved subsequently and independently.

He also sees this as a reason for the convergence of the pervasive spread of fus . èā and the adoption of linguistic innovations produced by cultural or political centers of influence, which speakers of language varieties perceived as languages of prestige.

Cohen's theory thus deals with convergence by contact as the end point. However, one issue that cannot be explained through this theory is the presence of common features between varieties of regions of the Arabic-speaking world that have never come into contact with each other [START_REF] Versteegh | Arabic language[END_REF].

The typology that has been adhered to by many scholars classifies Neo-Arabic varieties into six major dialectal areas, from East (Mashreq) to West (Maghreb):

1. Arabian Peninsula Arabic.

2. Mesopotamian Arabic.

Near-Eastern Arabic (Levantine).

4. Egyptian Arabic.

5. North African Arabic (Maghrebi). 17 6. Sub-Saharan Arabic However, a number of features and variations within these seem to transcend regional boundaries and effectively escape this typological enterprise. Classification by geographical area is relatively recent compared to other types of classification, such as sociological classification. Indeed, linguists and other observers of Arabic-speaking countries have long since shown that both the smallest locality and the most extensive region are traversed by a division between Qarab (nomadic) vs. h . aD ˙ar (sedentary) types. As mentioned in the previous section (1.1.1), the historical-linguistic tradition narrates two waves of Arabisation in today's Arabic-speaking areas: a first sedentary wave and then a Bedouin wave. This already assumes a first line of separation between sedentary dialect and Bedouin dialects, Qarab, which are also referred to as Badawī 17 Tunisia belongs to the North-African Arabic class, together with the varieties from Libya, Algeria, Morocco and Mauritania, and the current Maltese language, which was historically a Libyan-Tunisian variety [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF].

1.1. Historical and Linguistic Introduction dialects (meaning Bedouins). According to the same historical-linguistic tradition the second wave began in the middle of the 11th century and was led by three different tribes, including the Banū Hilāl, to whom we owe the appellation 'Hilali' for the Bedouin Neo-Arabic varieties that were formed through this second wave of Arabisation. While the Neo-Arabic varieties derived from the first wave, i.e. those referred to as sedentary, are also referred to as 'pre-Hilali', the distinction between sedentary and Bedouin comes back to [START_REF] Marçais | Comment l'afrique du nord a été arabisée[END_REF], who not only separates same into the two phases of conquests, but also into two social contexts. For example, the urban speech that he defines as being the heir to the old urban koiné:

'la langue des villes est l'héritière de la vieille koinè citadine remontant à la première conquête' 18 and a rural speech or villageois based on the Bedouin prototype, 'la langue des campagnes et des steppes repose sur le prototype qu'apportèrent avec eux les envahisseurs nomades du XIe siècle, Hilāl et Solaïm' 19 .

Regarding the latter, [START_REF] Marçais | Comment l'afrique du nord a été arabisée[END_REF] also adds that:

'La langue qu'on y parlait était et est demeurée un arabe barbare. Il était issu de la déformation de la koinè citadine par des paysans berbères' 20 .

He thus highlights how the prestigious variety of the urban koiné was exerting its influence on the rural variety. Among the mentioned sedentary languages, one must also take an additional type of speech into consideration: the speech of Jewish city dwellers (citadins juifs), which was, on the whole, a sedentary population's speech, and thus opposed the speech of the Maghrebi Bedouin population [START_REF] Grand | Eléments du système consonantique pré-hilâlien en arabe maghrébin: perspective historique[END_REF].

Following the above description, it is possible to outline the linguistic situation as follows:

1. Pre-Hilali or sedentary, (èaD ˙ar), from the 7th century.

(a) Urban speeches 18 'The urban language is the heir of the old urban koiné which can be traced back to the first waves of conquest'. My own translation.

19 'The rural language and language of the steppes are based on the prototype brought with them by the Bedouin invaders of the 11th century, Hilāl and Sulaym'. My own translation.

20 'The language spoken there was and has remained a barbaric Arabic. It came from the deformation of the urban koinè by Berber peasants'. My own translation. Regarding these classifications, it must be pointed out that it has recently come in for strong criticism. In particular, [START_REF] Benkato | From medieval tribes to modern dialects: On the afterlives of colonial knowledge in arabic dialectology[END_REF] states that all Arabic dialectology to date is strongly influenced by Marçais linguists (William and Philippe), who nevertheless shaped the categories on which the field of research is based. The same author mentions this issue in a later work, where he addresses the description of Maghrebi Arabic specifically, introducing it in the following terms: 'In many cases, first-layer varieties [Pre-Hilali] of urban centers have been influenced by neighboring second-layer ones [Hilali], leading to new dialects formed on the basis of inter-dialectal contact. It is important to note that North Africa is becoming increasingly urbanized and so not only is the traditional sedentary/nomadic distinction anachronistic (if it was ever completely accurate), but also that intensifying dialect contact accompanying urbanization means that new ways of thinking about Maghrebi dialects are necessary. It is also possible to speak of the recent but ongoing koinéization of multiple local varieties into supralocal or even roughly national varieties-thus one can speak, in a general way, of "Libyan Arabic" or "Moroccan Arabic"' (Benkato, 2020, 198).

These assertions found approval from a number of experts in the field, who had previously expressed their doubts about the adaptability of the traditional classification to Tunisian varieties, and in particular the concept of village dialects. Among these researcher, [START_REF] Mion | Réflexions sur la catégorie des «parlers villageois» en arabe tunisien[END_REF] defines village languages as varieties whose mixed nature is caused by contact with Bedouin Arabic, from which they have taken on their fundamental characteristics. Also D'Anna (2020) stated that the category of village dialects should be contextualised in a language contact framework that takes into account the importance of local history. 22However, as far as this classification into sedentary and Bedouin varieties by Marçais linguists is concerned, not only does it remain the most widespread and established methodology for comparing Arabic varieties, but there is no alternative so far. In particular, this methodology aims to observe the preservation, neutralisation, mutations or innovations of some Old Arabic (OA) traits in Neo-Arabic varieties. Below are proposed tables that simplify the traditional axioms. The outline refers to [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF], who presents classification criteria for Bedouin or sedentary Neo-Arabic varieties based on phonological, morpho-syntactic and lexical features. Regarding the lexical characteristics, since they are not central to this work, it is suggested to consult the studies of [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF] and Marçais (1950).

OA features sedentary v. Bedouin v. With regard to the commonly accepted distinctive phonological features presented in table 1.1, the first point concerns the phonetic realisation of the uvular plosive phoneme /q/, which is a voiceless /q/ in the sedentary varieties and is voiced /g/ in the Bedouin varieties. Regarding the second point, this is not the exact case in the Tunis koiné (Tūnsi), which is generally defined as a sedentary variety, but where the interdental phonemes are maintained (as in Bedouin varieties. This is in contrast to sedentary varieties (mostly for extra-peninsular Arabic), in which the fricatives /T D/ are realised as plosives /t d/ with the confluence of */D ˙/ in /d . /. Instead, in the Bedouin varieties, dental fricatives are generally maintained with the confluence of */d . / in /D ˙/. Regarding the last point, the phoneme that presents an affricate realisation ([Ã]) in OA seems to be conserved in Bedouin Neo-Arabic varieties along with the fricative realisation ([Z]). However, the latter appears to be the main realisation in sedentary varieties [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF][START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF][START_REF] Durand | Dialettologia araba[END_REF][START_REF] Mion | Pré-hilalien, hilalien, zones de transition. relire quelques classiques aujourd'hui. Mediterranean Contaminations. Middle East, North Africa, and Europe in Contact[END_REF] In contrast, an outline of the main distinctive characteristics of the morphological features employed to classify Neo-Arabic varieties in the Bedouin or sedentary type are reported in table 1.2. The first point concerns the gender distinction in the second-and third-person plural of verbs and pronouns, which is maintained only in the Bedouin varieties. The second point concerns the different realisation of the suffix for the thirdperson singular pronoun; while the third point deals with the axiom that the verbal time-aspect systems display different behaviours between the sedentary and Bedouin varieties, wherein the sedentary varieties employ a set of preverbs and innovative strategies for expressing verbal time and aspect [START_REF] Mion | Osservazioni sul sistema verbale dell'arabo di tunisi[END_REF]. In addition, the fourth point of the table concerns the difference in the verbal systems, and once again, it is the sedentary varieties, unlike the Bedouin ones, which exhibit an innovative system of passive verb realisation, which consists of employing prefixes instead of applying the vowel alternation recognised by the Old Arabic (OA) system (also known as ablaut or apophony). Both the dual form of nouns (the fifth point) and genitive phrases (the sixth point) illustrate the tendency of the Bedouin varieties to employ strategies which are also present in Old Arabic. Conversely, in the sedentary varieties, dual forms are mainly used in double body parts and dual forms which are already fixed in the lexicon, such as the Tunisian /Palfīn/, which has the literal meaning of 'two thousand', but which is used to express the amount of 'two Tunisian Dinars'. Regarding the genitive phrase, the sedentary varieties employ both the so-called construct state (the synthetical structure) and the pseudo-prepositional system (the analytical solution).

1. */q/ [-voiced] [+voiced] 2. */T D D ˙/ /t d d . / /T D D ˙/ (*/d . D . / -→ /d . /) (*/d . D ˙/ -→ /D . /) 3. */ǧ/ [Ã] /ž/ [Z] /ǧ/
The pseudo-preposition employed for the latter in Tūnsi is /mtāQ/ (where the etymology is */matāQ/ 'property'). The last two points (the seventh and eighth points) concern the agreement, the first dealing with non-human heads, while the second deals with number and gender agreement. In the first case, the solution referred to in OA (of female singular agreement for non-human heads) is also employed only by Bedouin varieties. In the matter of the second agreement, the inflexions seem to be complete for Bedouin varieties, but are incomplete for sedentary varieties, with the constituent's segment showing N pl.f +ADJ pl.m [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF][START_REF] Singer | Grammatik der arabischen Mundart der Medina von Tunis[END_REF][START_REF] Durand | Dialettologia araba[END_REF].

As previously discussed, dialect classification features do not produce sharp distinctions, either between regions or between sociological varieties. Few differences emerge within the same dialectal area, and regional marking is not very evident. As an example, let us take into account the study of [START_REF] Ritt-Benmimoun | The Tunsian hilāl and sulaym dialects. a preliminary comparative study[END_REF] into the differences between the Tunisian Neo-Arabic which is traceable to the H type (which is Banū Hilāl Arabic), in comparison to the S type (Sulaymi Arabic), for example, the Kasserine (Al-Qas . rīn) variety. She describes the affinity between the H and the sedentary varieties and cites the explanation given by Marçais (1957), i.e. that in regions where the H dialect is prevalent, the sedentary dialect had originally been spoken, but was subsequently overlaid by Bedouin (Sulaymi and Hilali) dialects, resulting in the Bedouin and sedentary varieties becoming merging. As Ritt-Benmimoun (2014) states, there are in fact transitional regions. As a result, no sociological koiné seems to have expanded across the entire Arabic-speaking area. However, legitimised by linguistic prestige, these urban areas were developing linguistic dynamics characterised by processes of homogenisation or differentiation and standardisation. It is therefore questionable as to whether a phonological trait can resist the centrifugal force of urban centers through the processes they mobilise, such as linguistic accommodation and dialectal levelling, and the strong pressure of the prestigious character of their local language [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF]. We will address this issue in terms of Tunisian Neo-Arabic in section 1.1.4.

The next section will focus on an analysis of the current reality of the Tunisian the Bedouins spoke Arabic according to their 'natural disposition' and did not need grammarians to tell them how to speak. However, in sedentary civilisation things have changed: decadence was embraced and the language threatened to become corrupted. In Ibn Khaldūn's description, this process of corruption was connected to the 'invention' of grammar [START_REF] Campanini | Ibn Khaldûn e la Muqaddima. Passato e futuro del mondo arabo[END_REF][START_REF] Embarki | Les dialectes arabes modernes: état et nouvelles perspectives pour la classification géo-sociologique[END_REF]. Ibn Khaldūn's views on the historical development of the Arabic language are an important testament to how the Arabs of the time viewed the history of their language and how this is reflected in today's Arabic linguistic ideology. The history of the Arabic language begins in the period prior to Islam (the 5th-6th centuries), commonly referred to as Ǧāhiliyya 'the period of ignorance (of God)', when the Bedouins had not yet received the message of Islam.

According to the view of Ibn Khaldūn, the pure language of the Bedouins remained unchanged until the Arabs came into contact with other peoples during the period of the conquests. Despite some known differences among the spoken language varieties, these differences did not break down the essential unity of the language. The Arabic language was used in interactions between the Arabs and the conquered peoples, but because of the difficulty of the language and due to these people's mistakes when learning the complicated linguistic structure, the Arabic language began to be corrupted.

The central theme of Ibn Khaldūn's historical reconstruction is this corruption of the language. This opposition between classical grammatical language and ungrammatical colloquial variants is still present in the Arabic world today.

Historical and Linguistic Introduction

Classical Arabic was the same language that was later recorded in writing by medieval grammarians, following two centuries of oral transmission of the poetic texts, as were the sacred texts. However, this linguistic form, which was used for poetic-literary purposes in the 6th and 7th centuries in central Arabia, could represent the crystallisation of a precise linguistic stage in the evolution of the language originally spoken by the Bedouin tribes mentioned above [START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF][START_REF] Mion | La lingua araba. Nuova edizione[END_REF]. Focusing more on poetic-literary and sacred texts, in order to record their norms, grammarians left a large part of the linguistic reality, which was most likely already configured to be diglossic.

The word diglossia, from the Greek διγλωσσία, describes a socio-linguistic situation in which two main forms of a language coexist, in which a high form of language (H) is perceived as older, more prestigious, purer, more beautiful, and perhaps more appropriate for high educational contexts, literature, religion, and so forth. The low language (L) has no prestige and is devalued and even despised, even though it is the actual mother tongue of the population, having been learned in the first years of life and being used by all members for colloquial purposes (at home, on the streets, in bars, and for humour). However, summarising the multiple complexities of the Tunisian linguistic reality with the word diglossia is entirely too reductive, as can be seen over the course of this chapter.

Ferguson's diglossic model [START_REF] Ferguson | [END_REF] is traditionally considered to be the academic father of the conceptualisation of diglossia, and his article represents a crucial step in explaining the functional coexistence and linguistic interrelationships between these varieties, a step that probably marks the beginning of Arabic socio-linguistics as an academic entity in its own right. In the article, Ferguson gave theoretical body and comparative generalisation to a situation that had already been recognised, for example by Psicharis (1888), who was the first to use the term diglossia in reference to the Greek linguistic situation. At the same time, the diglossic reality for the Arab world has been described by French linguists working in North Africa, first of all by William [START_REF] Marçais | Le diglossie arabe[END_REF], who described the coexistence of a langue littéraire that coincides with l'arabe écrit and a variety of arabe parlé in Algeria [START_REF] Benmamoun | The Routledge handbook of Arabic linguistics[END_REF]. The notion of diglossia was later refined by Ferguson himself and many other scholars. In fact, [START_REF] Ferguson | [END_REF] opened a Pandora's box full of extensive literature on the subject, which attests to the manifestation of diglossic situations and their widespread reach throughout languages across the world. 24 However, what has always been highlighted as a weak point of the Fergusonian model is a strict and fixed division that abstracts languages into linguistic models which are too rigid to coincide with reality. This has caused a move towards the search for further parameters to describe the contact and interactions between languages, such as the language continuum or the code-switching theories that will later be addressed here [START_REF] Lancioni | Insegnamento dell'arabo e certificazione: una panoramica[END_REF][START_REF] Bassiouney | Arabic sociolinguistics: Topics in Diglossia, Gender, Identity, and Politics[END_REF]. According to the ideal Fergusonian model, diglossia describes a stable socio-linguistic situation in which, in addition to a low variety L (coinciding with the colloquial variety of a language) which may include a standard or a regional standard, there is a high variety H, which is highly codified (and often grammatically more complex), complementary to the former, and acting as the medium for a vast and respected body of literature, from a previous period. This H variety, according to Ferguson, is learned largely through formal education and is used for most written and formal speech, but is not used in any sector of the community for ordinary conversation. [START_REF] Ferguson | [END_REF] identifies a set of nine parameters which can be used to describe a typical diglossic situation:

1. Function. This concerns the rarely overlapping uses of the H and L variety. The respective written and oral languages serve different functions, where the former is preferred for formal contexts and the latter for informal contexts.25 2. Prestige. This refers to the superior status of the H variety over the L variety as perceived by the speech community.

3. Literary heritage. This describes the coincidence of the literary language type with the H variety.

4.

Acquisition. This implicitly refers to the L variety being the native language of the speech community, describing the acquisition of the H variety through formal education.

Standardization.

A process concluded only for the H variety.

6. Stability. Referring to to the diglossic situation as being stable over time.

7. Grammar. Alludes to structural divergences between the H and L varieties.

8. Lexicon. The two varieties share a certain amount of vocabulary and semantic content.

9. Phonology. [START_REF] Ferguson | [END_REF] describes the divergence as follows:

'[T]he sound systems of H and L constitute a single phonological structure, of which the L phonology is the basic system, and the divergent features of the H phonology are either a subsystem or a parasystem [...] if pure H items have phonemes not found in pure L items, L phonemes are frequently substituted for these in oral use of H and regularly replace them in tatsamas.'26

The appeal of Ferguson's model is that it provides an explanation for the maintenance of more or less structurally divergent forms in the same speech community.

It is noteworthy that Ferguson's model incorporates spoken language, thus marking a departure from the Arabic philological tradition which was primarily oriented toward the interpretation of written texts. Notably, Ferguson not only described contemporary diglossia, but also projected its origins back to the time of the early seventh-century Arab diaspora. In addition, [START_REF] Ferguson | The Arabic koine[END_REF] argued that modern dialects developed from a koiné born in the military camps of the Arabs. This contact produced a certain simplification and levelling among the dialects, with a variety similar to Old Arabic continuing to be spoken, at least for a time, among Bedouins. This aspect will be returned to in more detail within section 1.1.2.

As proposed by [START_REF] Badawī | Levels of modern Arabic in egypt. Cairo: dar al-ma'arif[END_REF] and then demonstrated by further studies [START_REF] Bassiouney | Arabic sociolinguistics: Topics in Diglossia, Gender, Identity, and Politics[END_REF][START_REF] Brustad | The syntax of spoken Arabic: A comparative study of Moroccan, Egyptian, Syrian, and Kuwaiti dialects[END_REF], the Arabic language can be seen as a continuum, ranging from Classical Arabic (fus . èā) on the one hand as the highest and most formal linguistic variety, to Qāmmiyya, or colloquial Arabic (the Neo-Arabic varieties also known as

Neuarabisch), on the other. An intermediate variety known as Modern Standard Arabic (MSA), falls between the two forms, and is used mostly in news broadcasts and 1.1. Historical and Linguistic Introduction formal interactions, but is rarely spoken elsewhere and is not learned by anyone as a native language. The Egyptian linguist Badawī points out that the boundaries between these categories are fluid, and native Arabic speakers are able to adapt their language forms according to context and their communication needs.

Badawī's diglossic model [START_REF] Badawī | Levels of modern Arabic in egypt. Cairo: dar al-ma'arif[END_REF] was the first to propose the vision of a continuum of linguistic variation, proposing a socio-linguistic representation of the situation in the urban area of al-Qāhira. His work built on the idea of the high and low varieties of [START_REF] Ferguson | [END_REF] by adding more levels, firstly identifying two levels by splitting the H variety into Classical Arabic (CA) and Modern Standard Arabic (MSA), and then by identifying three gradually less formal varieties, as shown in the list reflecting his pyramid model.

1. At the top, the two levels of fus . èā:

(a) at-turāT, the Classical Arabic of the pre-Islamic literary tradition (classical heritage).

(b) al-Qas ˙r, the contemporary Arabic fus ˙èā better known as Modern Standard Arabic (MSA).

2. At the positions below, three levels of Qāmmiyyat:

(a) al-muTaqqafīn, which was spoken by highly educated native Arabs, comparable to what [START_REF] Mitchell | What is educated spoken Arabic?[END_REF] defines as Educated Spoken Arabic (ESA), which will be further addressed later (the colloquial variety of the cultivated people).

(b) al-mutanawwirīn, which was used by people who have received a generic education (the colloquial language of the basically educated).

(c) al-Pummiyyīn, the one spoken by people who have not received any kind of linguistic education (the colloquial language of the illiterates).

The crucial aspect of Badawī's model is the fact that his subdivision implies both a stylistic (regarding register) and socio-economic (education) hierarchy, which ends up being a more diastratic than diaphasic subdivision [START_REF] Bassiouney | Arabic sociolinguistics: Topics in Diglossia, Gender, Identity, and Politics[END_REF]. Specifically, as [START_REF] Durand | Dialettologia araba[END_REF] explains, it is very difficult to consider that uneducated people can 1.1. Historical and Linguistic Introduction use the fus . èā linguistic registers according to the context needs, whereas an educated person can be expected to have a certain freedom in adapting their register to formal and informal situations.27 As a result, this is a pyramidal model, especially when taking into consideration the amount of speakers belonging to each language class. However, the linguistic situation of Arab countries can also be described using a monosystemic model such as that of [START_REF] Hary | The importance of the language continuum in Arabic multiglossia[END_REF] discussed below.

Mitchell

In the mid-1970s, an alternative view of the study of linguistic variations in spoken Arabic emerged. [START_REF] Mitchell | Educated spoken Arabic in egypt and the levant, with special reference to participle and tense[END_REF] developed the concept of Educated Spoken Arabic (ESA), which lies on the continuum between the two poles of the acrolects (Standard Arabic and Literary Arabic) and the basilect (Vernacular Arabic). This ESA linguistic variety corresponds to Badawī's Qāmmiyyat al-mutanawwirīn/muTaqqafīn being a form of spoken Arabic, which was only used by educated people. Educated Spoken Arabic has also been described by [START_REF] Meiseles | Educated spoken Arabic and the Arabic language continuum[END_REF] as a national vernacular type characterised by the aspirations of its speakers to get rid of local features through a process of koineisation and/or borrowings from literary Arabic. A type of al-luGa al-wust ˙ā, in this case meaning 'middle Arabic', is the informal variety used among educated Arabs and for inter-dialectal communication [START_REF] Durand | Dialettologia araba[END_REF]. Indeed, it consists of a hybrid form of standard and colloquial Arabic elements. However, according to the Durand, Arabic is in fact never 'pure', but is instead constantly subjected to mediation between the two poles, driven by the requirements for interaction. It is an intermediate style that varies from speaker to speaker, even according to their competence in Standard Arabic [START_REF] Bassiouney | Arabic sociolinguistics: Topics in Diglossia, Gender, Identity, and Politics[END_REF]. The main difference between the ESA described by [START_REF] Meiseles | Educated spoken Arabic and the Arabic language continuum[END_REF] and that described by [START_REF] Mitchell | What is educated spoken Arabic?[END_REF] is that the latter is not more committed to the idea of discrete levels of Arabic(s). In fact, [START_REF] Mitchell | Educated spoken Arabic in egypt and the levant, with special reference to participle and tense[END_REF] explains that a certain amount of overlap may occur between ESA and MSA or ESA and vernaculars, as for example in the SVO order, where in the second case, he describes the tendency to level two vernacular forms for the purpose of effective communication or to respect the formal stylistic code. 28 [START_REF] Hary | The importance of the language continuum in Arabic multiglossia[END_REF] defines multiglossia as the linguistic situation in which different varieties coexist side by side in a language community and where each one is used in different circumstances and with different functions. Hary further describes the arrangement of such varieties as being spread along a context-dependent continuum where speakers (and writers) continually move from one system to another. Hary's proposal thus consists of a monosystemic model (rather than a dichotomous one with a sharp division between the H variety and the L variety), where the two ends of the language continuum coincide with the standard acrolect variety and the colloquial basilect variety. Along the continuum, between the two extremes, we can also find the mesolect, where innumerable varieties can be placed, which are used by native speakers in different circumstantial situations. Moreover, the native speakers themselves can make different combinations among the varieties, sometimes unconsciously, which is why the system outlined by Hary appears highly dynamic. Its dynamism is determined by a number of sociolinguistic and linguistic variables:

Hary

1. Setting. Formality or informality of the communication context.

2.

Topic. The topic may require more or less seriousness.

3. Speakers' skill in MSA. Speaker competence in MSA is a necessary condition for the dynamism of discourse across different levels.

4.

Emotional state of the speakers. According to Hary, a particularly emotional speaker (nervous, angry, etc.) tends to move toward the colloquial pole.

5.

Participants in the discussion. The expertise of other participants in MSA is also a prerequisite for dynamism.

6. Function of the discourse. The function of speech is also the basis of this model, as it was for previous diglossic models.

7. Personal relationship with the audience. This is a component underlying all speech acts, i.e. the building of relationships with interlocutors, and the building of communicative solidarity between speakers.

It is also necessary to consider the continuous mutability of linguistic systems, such as the historical and political, economic and social transformations that the linguistic landscape of the Arab world has undergone in the last three decades. Among these transformations, the advent of technology and the birth of Computer-Mediated Communication (CMC) has added further nuances to the already colorful picture, as seen in section 1.3.1.

The relationship between bilingualism and the concept of diglossia

Regarding the relationship between diglossia and bilingualism, [START_REF] Fishman | Bilingualism with and without diglossia; diglossia with and without bilingualism[END_REF] offers an overview of linguistic realities which feature both concepts, as in Tunisia, reorganising the two concepts. According to Fishman, the latter refers to the use of more than one language in a society, while the former refers to the functional distribution among them.

Therefore, it is possible to have bilingual societies in which the two languages are used for parallel functions (without diglossia), as well as those in which different languages correspond to different communicative functions (this is the case, for example, of the Yiddish society in Germany). Conversely, there are diglossic communities that are also bilingual. Many of the communicative and symbolic functions performed by the L and H diglossic varieties may be performed by dialects and the standard language or by other languages unrelated to the H and L varieties. [START_REF] Mejdell | Diglossia[END_REF] makes an important remark about the different point of observation of the linguistic sciences by noticing that from a purely sociological or ethnographic perspective, the specificity of diglossia may not be relevant, whereas for a more linguistically oriented analysis of language contact, variation, and change, the linguistic analysis of diglossia that opposes two related and highly divergent varieties becomes central in order to study their differences. More precisely, what these distinctions consist of and how these develop alongside societal changes must be taken as the central theme.

Historical and Linguistic Introduction

Switching theory instead of diglossic continuum

An alternative view to that of the discrete levels or the diglossic continuum individuation began to be investigated just before the 1990s through contact linguistics. In particular, the code-switching theory, the most widely used model of which is known as the Matrix Language Frame (MLF), was developed by [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF] and later updated to the 4-M model by [START_REF] Myers-Scotton | Testing the 4-m model: An introduction[END_REF], as is explored in section 1. 3.3. The distinction between code-switching and code-mixing is far from clear. The most common understanding of these concepts is that code-mixing is the alternation of languages at a lower level of the sentence (or clause) and code-switching is at a higher level, and the former is mainly attributed to grammatical and structural reasons, while the latter is accredited with a greater discursive and communicative importance. The term code-switching is used by [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF] to refer to alternations between linguistic varieties within the same conversation. According to the MLF model, every conversation between bilingual speakers presents a Matrix Language (ML) within which the elements of an Embedded Language (EL) are inserted. An ML can be identified on the basis of indicators of a predominantly morpho-syntactic nature: the type of syntactic structures used, the relative quantity and frequency of morphemes of one or the other language present in the language segment analysed, the order of the constituents, the type of morphemes of one or the other language used by the speakers, etc. Eid (1982,1988) was the first to apply the code-switching model to the Egyptian diglossic situation. Regarding Tunisian Neo-Arabic, [START_REF] Attia | Differents registres de l'emploi de l'arabe en tunisie[END_REF] identifies code-switching between MSA and Tunisian Neo-Arabic, including morphological and lexical features, such as the Tunisian relative marker and the preference of the Tunisian verb /n@žž@m/, 'I can' instead of the MSA /Pastat . iQu/. Moreover, [START_REF] Boussofara-Omar | Neither third language nor middle varieties but diglossic switching[END_REF] argues that what is defined as middle Arabic by [START_REF] Hary | Middle Arabic: Proposals for new terminology. al-'Arabiyya[END_REF], ESA by [START_REF] Mitchell | Educated spoken Arabic in egypt and the levant, with special reference to participle and tense[END_REF] and [START_REF] Meiseles | Educated spoken Arabic and the Arabic language continuum[END_REF] or Qāmmiyya l-muTaqqafīn by [START_REF] Badawī | Levels of modern Arabic in egypt. Cairo: dar al-ma'arif[END_REF], is neither a level nor a section of the continuum as described by [START_REF] Hary | The importance of the language continuum in Arabic multiglossia[END_REF][START_REF] Hary | Middle Arabic: Proposals for new terminology. al-'Arabiyya[END_REF], but is instead the result of a switch between MSA and Tunisian Neo-Arabic, making diglossia a particular case of language contact. Tunisian-French code-switching has also been addressed for example by [START_REF] Riahi | Emploi de l'arabe et du français par les élèves du secondaire[END_REF]; [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF]; [START_REF] Sayahi | Code-switching and language change in tunisia[END_REF], the first of which confirmed the extensive use, by Tunisian educated speakers, of what [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF] later came to define as franco-arabe, as can be seen in the next section. Regarding [START_REF] Sayahi | Code-switching and language change in tunisia[END_REF], same confirms that Tunisian speakers with a higher education (university-level education) exhibited a much higher frequency of code-switching that reflects their higher degree of competence in the French language.

The Tunisian multilingual situation

According to the Tunisian scholar [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF], Tunisian native speakers exhibit the use of five different levels of variables:29 1. Classical Arabic (CA) + written.

Literary Arabic (LA)

3. Modern Standard Arabic (MSA) 4. Educated Arabic (EA) 5. Tunisian Arabic (TA) + spoken. This level includes all diatopic variations of TA. [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] thus organises the variables into levels along a vertical continuum, in a manner similar to [START_REF] Badawī | Levels of modern Arabic in egypt. Cairo: dar al-ma'arif[END_REF], asserting that the literature on the concept of diglossia diverges from the general perception in the Arab world of the Arabic language as a unicum, and that linguistic labels are only useful as descriptive terms of the function that particular variables play in society. He continues to explain that CA, the highest variety, coincides with the sacred texts of the QurPān and generally with Ǧāhiliyya period texts. In Tunisia, the diffusion of Classical Arabic has been sustained until today through a national network of lectures held in mosques (kuttāb), which continue to offer a valid, officially recognised alternative to kindergarten. CA is still maintained and used in the following religious practices today:

1. The reading or recitation of the QurPān, which is generally included in daily religious practices; 2. Prayer in the mosques, run by the Imams, who along with scholars of the religion (QulamāP) continue to be trained in the best classical tradition at the University of Zaytouna (ŽāmiQat @z-Zītūna), which is a fully-fledged component of the higher education system of modern Tunisia;

3. The spread of religious books (mainly the exegesis of the QurPān and the Prophet's teachings).

Literary Arabic (LA) initially appears in the Arabic production of literary, rhetorical, philosophical and scientific works by Arab-Muslim authors of the early Islamic period until the fall of the Abbasid caliphate (and the beginning of the decline of Arabic-Islamic civilisation). This variety, which is described as a manifestation of the CA of great works by [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF], was later taken up by authors of the 19th and 20th centuries. It is still taught in Tunisian schools as part of the Arabic language and literature curriculum, but remains restricted to literary and academic circles.

MSA is an intermediate functional register that has evolved into a lively written and oral variety. This is the language of the mass media, international politics, as well as modern plays, novels, literary magazines, and lectures. Among the 'high' varieties described by [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF], MSA is the first to have dynamic characteristics which have allowed it to evolve through the incorporation of terms which are necessary for the description of modern reality, such as scientific, technological and commercial vocabulary. As for the inclusion of these terms in MSA, sometimes they are loans from French or English, but they may also be neologisms based on the reworking of Arabic material. 30 EA is the last intermediate variety before colloquial Tunisian Arabic (TA), with respect to which it ranks as a higher variant as it is characterised by its borrowings from CA, LA, and MSA. [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] describes it as a strictly oral form, used by educated Tunisians in formal and semi-formal contexts such as meetings or negotiations, and in conversations involving learned topics and educated interlocutors, perhaps from other Arab countries. 31 Daoud also challenges the definition of EA by [START_REF] Walters | Gender, identity, and the political economy of language: Anglophone wives in tunisia[END_REF] as a sociolect. Walters defines EA as a new social (i.e. class) dialect, and adds: 'as children who live in urban communities, composed of Tunisian couples in which both wife and husband are well educated, grow up acquiring it as their native variety.' 30 With regards to the strategies applied in order to update MSA and in the meantime preserve its purity, see [START_REF] Mion | La lingua araba. Nuova edizione[END_REF]. 31 As we will see below, it is in fact not so strictly oral.

In contrast, when referring to TA, Walters says that it:

'indexes home and hearth, daily life, everyday logic and lived experience, and common sense.'

However, according to Daoud, EA does not draw on one's social background, but only on one's level of education. However, an objection to this has arisen, namely that the level of education constitutes one (if not the) discriminating factor underlying social inequalities in a country that still has, to date, a wide socio-economic divide. In fact, [START_REF] Walters | Gender, identity, and the political economy of language: Anglophone wives in tunisia[END_REF] himself concludes his explanation of EA (which he refers to as ETA from Educated Tunisian Arabic) by saying that it is an intermediate variant between

L and H, leaning toward the L pole of the diglossic continuum, and that it constitutes an emerging variety, which is connected to the social changes that have been set in motion since independence.

Regarding TA, i.e. the low variety, [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] defines it as the oral dialect of Tunisia. He includes both local and regional varieties, characterised by phonological and lexical variations within the country, in the same level, in consideration of the fact that, despite the diatopic variation, TA remains distinguishable from the dialects of neighbouring countries, such as Algerian and Libyan, most likely due to the cumulative effect of contact with other languages since antiquity.

Daoud also emphasises the oral nature of this variety of Tunisian Arabic, specifying that there is a small archive written in the Tunisian dialect, but that it consists mainly of folk tales, collections of proverbs and poems, occasional contributions from the press, and some novels, such as Antoine de Saint-Exupéry's novel: Le Petit Prince, which was translated in 1997 by Hédi Balegh, a Tunisian intellectual known for his militancy in favor of recognition of , @d-dārža, the North-African name for the Qāmmiyya [START_REF] Mion | La versione del piccolo principe in arabo tunisino. Ricerca e didattica tra due sponde[END_REF]. However, it is useful for the purposes of this study to keep in mind the distinction between:

1. The national TA, which is oriented towards the urban koiné of Muslim Tunis.

2. The local varieties of TA.

In fact, as later explored in this chapter (section 1.1.4), there have been occasional calls to make national TA the official language of Tunisia, but these have never been This reference is to the graphemic system which is here referred to as Arabizi. However, before addressing the issue of digraphia (in section 1.3.2) in the context of Tunisian social networks, it is necessary to first describe Tunisian bilingualism.

The systematic diffusion of the French language through Tunisia began with the protectorate (1881). However, the independence obtained by Tunisia in 1956 has done nothing to reduce the role of this language in Tunisian society. In fact, according to [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF], its prestige has increased in the eyes of large proportion of the population for several reasons: first of all, the liberation from the 'colony complex', which is a source of hostility for the language of the oppressor; moreover, among the youngest generations, who have never known linguistic opposition of a nationalist nature, French has become a source of political and socio-economic privileges and a means of social progress. In fact, French extends across the formal spheres of education, business and administration to the informal spheres of everyday oral communication. [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] analyses the French language in Tunisia in terms of diglossia, separating metropolitan French (the high variety of French) from the North African varieties of French (the low variety). The former, according to Daoud, is used by Tunisians who have had contact with the French language from France, having spent a period there for study or family reasons, or having attended les écoles de la mission culturelle française (French schools). These are opportunities for contact which are open only to a minor part of Tunisian society, since they require economic resources that most of the population does not have. Among the members of this elite with access to H-level French, as reported by [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF], it is not unusual to find Tunisians who are poorly proficient in MSA. French has thus become the language of high society, intellectuals, and senior executives. Speaking French, especially good French, is also a claim to prestige, an act of identity affirmation and a declaration of belonging to the Tunisian elite. This act may be the imitation of French pronunciation or the French accent, or in the 'French-like' pronunciation of the Tunisian itself, typically in urban society, particularly in the capital, and even more so in the female genderlect. Regarding the L-level French, [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] expresses the idea that it is only North African French within which it is possible to find different shades of usage for different communicative functions. This seems to coincide with the variety that [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF] defines as le franco-arabe, which, according to him, consists of a much more complex mélange d'arabe dialectal tunisien et de français. In fact, this variety goes beyond the level of lexical interference to encompass syntactic interference in a complicated and curious linguistic tangle.

What [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF] claims is 'paradoxical' is that such a mélange came about after independence. However, as illustrated above, there were other social motivations that explain the spread of French in Tunisia once freed from the colony complex.

Another important point that [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF] makes is as follows:

'Historiquement, un tel registre est appelé à disparaître tôt ou tard, une fois l'enseignement et l'administration arabisés.'33

During the seventy-five years that Tunisia was under the French protectorate , the Arabisation of teaching and administration and public life was claimed unanimously and numerous times by Tunisian political parties and trade unions. For example, national legislation has not regulated the language of signage. Only an order

given by the municipality of Tunis (August 6, 1957) obliges the owners of public es- [START_REF] Daoud | The language situation in tunisia[END_REF]. He offers a detailed analysis of Tunisian language planning starting immediate in the post-independence period. With regard to the 1958 reform, this was aimed at unifying the various school systems which were present in Tunisia (recalling the widespread role of mosques in dispensing first grade instruction) under a single bilingual system managed by the Ministry of National Education and offered free of charge. The reform also envisaged the adoption of the organisation and national curriculum of the French system, complete with the baccalauréat as the final exam. An important point of the reform was the reinstatement of Arabic (mainly MSA) as the language of instruction, with the exception of elementary schools. However, in secondary schools and universities, the French language was relegated to a vehicle for teaching scientific and economic subjects. This reform marked the beginning of a campaign of Arabisation, which was implemented even more vigorously and systematically in 1976, but which did not bring about the desired results. 35 The biggest failure was the results of the 1986 baccalauréat exams, which President Bourguiba ascribed to students' weak proficiency in French, implying that Arabisation had been pushed too fast, or too far. [START_REF] Laroussi | Les politiques linguistiques des pays maghrébins un essai d'évaluation[END_REF] states that in Morocco, as well as in Algeria and Tunisia, Arabisation was presented as a legitimate operation with the aim of providing MSA with the necessary tools to assert its ability to convey all sorts of knowledge and to ensure equal opportunities to the Maghreb populations. However, in reality, according to Laroussi, the objective behind Arabisation was to eradicate the French language, 34 'All commercial, industrial or other signboards that overlook the public highway must be written in Arabic. However, they may be bilingual.' My own translation. 35 The best result achieved by these reforms, according to [START_REF] Daoud | The language situation in tunisia[END_REF], seems to have been the acquisition of Tunisian students' passive competence in both MSA and French, such that they were able to understand texts and follow lectures in these languages, but not to improvise a spontaneous conversation.

considering it the language of colonialism. Indeed, both [START_REF] Daoud | The language situation in tunisia[END_REF][START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] and [START_REF] Baccouche | L'emprunt en arabe moderne[END_REF] attribute the failure of Arabisation to the strong attachment of the elite to the French language and its cultural value system, which is why the political elite itself was unable to adequately promote an organised and planned process of Arabisation. The elite themselves were probably not even really interested in promoting it at the expense of bilingualism and biculturalism, let alone at a time when Islamic fundamentalism represented a realistic challenge to the leadership that took power in 1987.

' [L]es choix politiques en matière linguistique ont été un facteur déterminant dans la consolidation du bilinguisme et l'accentuation du prestige de la langue française en Tunisie, vu les multiples fonctions qu'elle n'a cessé de remplir dans la vie sociale, économique et culturelle.' (Baccouche, 1994) 36 In 1988, several political parties and prominent national organisations signed the Pacte National, a document reaffirming the importance of the Arabic language in a global context. The call was aimed at reinforcing the Arabic language in order for it to become the language of communication, administration and education, and the national language, keeping Tunisia open to dialogue with other civilisations and languages.

'[W]e must strive, in this regard, to avoid the split between the elite and the popular masses.' 37 Among the decisions announced by President Zine El-Abidine Ben Ali (Zīn @l-Q Ābidīn ben QAlī), in 1994, the following objectives were put forward:

1. Reinforce functional literacy in Arabic (the national language), namely MSA.

2. Improve functional competence in foreign languages, particularly French and English.

Generalize computer skills.

36 'Political choices in linguistic matters have been a determining factor in the consolidation of bilingualism and the increase in the prestige of the French language in Tunisia, in view of the multiple functions that same has never ceased to fulfill in the social, economic and cultural life.' My own translation.

37 Pacte National, 1988;[START_REF] Daoud | The language situation in tunisia[END_REF].

In October 1999, the Prime Minister sent a circular to public officials stating that all official acts of public administration should be converted into Arabic and outlining the regulation of same, for both internal and external documentation. The deadline for the Arabisation process was December 2000. The really short deadline was credible according to [START_REF] Daoud | The language situation in tunisia[END_REF], to whom we refer for further discussion.

The only real concrete result of the Arabisation process is the fact that French has remained the language of academic and professional contexts, and that only the upper-middle classes use it for everyday conversations, especially women [START_REF] Daoud | The language situation in tunisia[END_REF], despite countless socio-political debates on the topic of Arabisation and Francophilia.

The Tunisian elite that led the struggle for independence from France and then took the reins of power made ambivalent choices on language policy and planning that, on the one hand, promoted the Arabic language as the anchor of Tunisian identity and, on the other hand, maintained French as the language required for gaining access to the scientific and technical and sociocultural knowledge of modernity. [START_REF] Daoud | The language situation in tunisia[END_REF][START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] shows that these choices actually had deeper historical roots that concealed a consensus among Tunisia's elite, including political and union leaders, to institutionalise Arab-French bilingualism and biculturalism. These choices are still adhered to today. The sensitivity of socio-linguistic issues together with the tension and passion they generate, are still a current source of concern, particularly with regard to the so-called langue mixte franco-arabe, the Tunisian koiné of the Tunisian bilingual elite. A more in-depth exploration of some of the socio-linguistic issues related to this are included in the next section. Another consideration is that French is the native language of second and third generation Tunisians in France. Many Tunisians emigrated as political opponents to the Ben Ali dictatorship, before returning to Tunisia following the fall of the regime.

This diaspora generated an impact on linguistic practices in Tunisia, as it revived the role of French as a common language, such that it no longer served as an emblem of the elite, and was instead seen as a language of international mobilisation and Tunisians from the diaspora, as well as Tunisians abroad (Allal and Geisser, 2018). Regarding the latter, if they are of the second or third generation, it is very rare for them to be competent in Arabic. Sometimes they can speak a little Tunisian, but they can hardly write or read it in Arabic characters, as also reported by [START_REF] Caubet | Vers une littératie numérique pour la darija au maroc, une démarche collective[END_REF]. For them, it was instead much easier to write in Arabizi, which will be explored in section 1. In recent years, the Tunisian language situation has been quietly changing and

French is giving way, not to Arabic, but to English. Young Tunisians are increasingly choosing to invest time (and money) in achieving proficiency in this language, which is the current language of science and technology. An example of this phenomenon as seen in the new generation is represented by Amel Bedhyefi, a booktuber that publishes videos where she's speaking about English books in a mixture of English and Tunisian. 39

The motivation for this can perhaps be the fact that the new generations no longer recognise themselves in the French cultural model in comparison with the American one, the language of which is instead experienced in a more modern and functional way in terms of the professional path. 40

38 Unfortunately, neither the origin nor the informant's country of residence was reported. 39 Here is the link an interview with her developed by Le Courrier de l'Atlas: https://www. youtube.com/watch?v=gDKoFm5DYko&ab_channel=LeCourrierdel%27Atlas. Consulted on 4th April 2021.

40 For a more in-depth look at the rivalry between French and English in Tunisia, which, despite being a particularly interesting topic, is not central to this work, refer to the study developed by [START_REF] Daoud | The language situation in tunisia[END_REF][START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF].

The Tunisian κοινὴ

'koiné /"kOIni:/ (n.) The spoken language of a locality which has become a STANDARD language or LINGUA FRANCA. The term was originally used with reference to the Greek language used throughout the eastern Mediterranean countries during the Hellenistic and Roman periods, but it is now applied to cases where a vernacular has come to be used throughout an area in which several languages or dialects are spoken, as in such notions as (for Old English) 'West Saxon literary koiné' or (for US-influenced British English) 'mid-Atlantic koiné '. 41 Following the definition of the term 'koiné' given in [START_REF] Crystal | A Dictionary of Linguistics and Phonetics[END_REF], it can be seen that this term was originally applied to the Attic dialect, which quickly spread as an official language. Indeed, the original koiné was generally spoken by educated Greeks, although they still used their local dialect among themselves. This term has often been used to describe language varieties that were not always similar in either form or function to the original Greek koiné. The term 'koineisation' has more recently been applied to the process of levelling that can result in a koiné. In this context, the term 'levelling' refers to reduction or linguistic cleansing of all the marks which are characteristic of a specific variety; it represents a kind of flattening of the representational power of a specific micro-culture in favor of regularity, simplicity of use and national representational power, spread across regional boundaries.

As far as we know, the first to use the term 'koineisation' was [START_REF] Samarin | Salient and substantive pidginization[END_REF].

Samarin describes this process as 'dialectal mixing': 'What characterises them [koinai] linguistically is the incorporation of features from several regional varieties of a single language. This kind of amalgamation (or dialect mixing) can lead to a certain amount of heterogeneity.

That is, a koiné, caught at an early stage of its history, might consist of many kinds of speech that are not easily correlated with non-linguistic factors like region, function, social status, etc.' However, he explains 'koineised colloquial Arabic', as previously defined, as being a type of 'levelling'. In his view, this levelling occurs in situations of 'interdialectal contact' when speakers attempt to suppress typical features of the local variety in favour of features that are simply more common, and more familiar. Indeed, he mentions the words of [START_REF] Blanc | Style variations in spoken Arabic: A sample of interdialectical educated conversation[END_REF]: 'In certain situations, usually interdialectal contact, the speaker may replace certain features of his native dialect with their equivalents in a dialect carrying higher prestige, not necessarily that of the interlocutor ... Moreover, levelling devices may be called into play without the speaker actually stepping out of his native dialect, but by selecting from among a number of equivalent features available to him those which are more general or more urban and suppressing those which sound local or rustic ... In general, levelling often takes place not so much in imitation of a specific dialect as in an attempt to suppress localisms in favour of features which are simply more common, more well known.'

What can be deduced from Samarin's study is that, for him, the result of a koineisation process is an individual koiné, which is determined on contextual needs. Furthermore, section 1.1.2 includes a reconstruction of the process of emergence and the diffusion of dialects, and it was noted that this is still an open and debated issue, not only in academia. In fact, it is such a lively issue that nowadays it remains at the center of heated discussions (even on social networks). Despite scholarly disagreement on the historical reconstruction of the emergence of dialects (the theory of monogenesis presented by [START_REF] Ferguson | [END_REF], koiné as an endpoint by [START_REF] Cohen | Etudes de linguistique semitique et arabe (Studies of Semitic and Arabic Linguistics)[END_REF], and the process of pidginisation by [START_REF] Versteegh | Pidginization and creolization: The case of Arabic[END_REF]), scholars seem to mostly be in agreement on the importance of contact between soldiers, as well as the idea of prestigious urban centers exerting their influence in the process of Arabisation and playing an important role in the formation and spread of 'modern' dialects. The influence of prestigious centres is a dynamic which has remained valid over the centuries. As pointed out by [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF], in the post-colonial phase, i.e. since the 20th century, major city dialects are often emerging as national or regional standards in both the Maghreb and the Middle East. In fact, in French Tunisia, as seen in section 1.1.3, political power was in the hands of foreign rulers and the Tunisian elite (regarding the latter, the elite remained so anchored to the political role it had at the time of the protectorate that it adopted the language of the occupiers as a distinctive feature [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF]). However, follow-1.1. Historical and Linguistic Introduction ing independence, Tunisian society was reorganized, leaving more space for political groups, but above all for local identities, or at least for politically prevalent ones. The history of the 20th century in Tunisia is explored in more depth below (section 1.3).

In the same century, the phenomenon of urbanisation meant that the main cities, particularly Tunis, welcomed Tunisians from all over the country, a phenomenon particularly related to the process of koineisation leading to a rise in literacy levels [START_REF] Holes | Community, dialect and urbanization in the Arabic-speaking middle east[END_REF]. [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF] describes the processes of koineisation that took place in the Arab world through five different modalities, regarding the modality that concerns the Tunisian process, she stated: 'Old urban centers with a declining urban elite and an important population renewal. The old urban dialect is no more prestigious enough to be acquired by new-comers and is even declining among the young generation of the original urban dwellers. The new-comers adopt instead the national urban koiné, and urban dwellers tend to speak this urban koiné in public space, keeping their own vernacular at best for family communication. This is the case in many North African cities like Fes, Tangier, Rabat, Tlemcen, and even Tunis, where the old urban vernacular tends to become more and more restricted to old women and is associated with an effeminate way of speaking.' Therefore, it is possible to affirm that the Tunisian koiné of Tunis (Tūnsi ) does not coincide with the old dialect of Tunis' old city center (Tunis medina), but rather with an urban koiné which emerged in the 20th century. In particular, that which may be perceived as the 'national' variety, which takes up Blanc's description above, appears 'more general or more urban and suppresses those that sound local or rustic'.

Regarding the differences between the old and the new Tunis-koiné, for example, Singer In section 1.3.1, we will examine how communication technology is supporting the spread of this national koiné. Before that, however, it is necessary to point out some not irrelevant aspects concerning the status of Tunisian contemporary koiné.

Based on the definitions of the koineisation process given by scholars, such as [START_REF] Samarin | Salient and substantive pidginization[END_REF], [START_REF] Blanc | Style variations in spoken Arabic: A sample of interdialectical educated conversation[END_REF] or scholars mentioned in section 1.1.3, it appears that koineisation involves the mixing of features of different dialects, and leads to a new dialect of compromise, which is not yet a standard dialect (considered to be the result of a top-down regularisation, see section 1.2), but is something similar to a Lingua Franca (see section 1.1.1), being the result of an agreement among speakers of the individual contributing dialects, which may or may not be maintained over time. In other words, as expressed by [START_REF] Siegel | Koines and koineization[END_REF], koineisation is a process that involves contact between linguistic subsystems, even if that contact does not always bring about koineisation.

There must be certain socio-linguistic conditions for a koiné to emerge from contact.

These socio-linguistic conditions relate to the nature of the subsystems involved in contact. The scholar defines subsystems as different linguistic varieties, which are genetically related to the same linguistic system and therefore also typologically similar enough to be adequately reflected in the following two criteria:

1. Being mutually intelligible; 2. Sharing the same language system, national standard language or literary language as superior.

Although a widespread perception is that of the dialect being 'younger' than the classical language, as discussed above, the discourse on the diachronic evolution of modern dialects is still open, so instead of referring to a genetically superior language, we must instead refer to a language perceived as superior in terms of diglossia. The two parameters, along with the scholars' definition of the koineisation process, seem to be appropriate for both the interlanguage process of formation of the military camps in 42 'It differs from the general urban-Tunisian koiné of the whole region to the ears of population groups that do not belong to it due to a certain pretentious manner of articulation (e.g. a strong Imāla [the shifting from /a/ ∼ /ā/ towards /i/ ∼ /ī/, in a not-marked context, that will be addressed in section 1. 3.3] sometimes also used by men, who occasionally similarly articulate the diphthongs found in women's language) and a whole vocabulary of their own, which is not used outside of this smaller group.' My own translation.

Iraq, on which some scholars agree, and the process described by Miller for the modern koinai of Tunisian Neo-Arabic.

As previously mentioned, however, koineisation is not a necessary process. In fact, speakers of different varieties can be in a situation involving stable contact, with welldefined social and functional roles, for long periods without ever reaching a levelling off. In addition to koineisation, other different processes can occur, such as diffusion for example, which involves the transfer of linguistic aspects beyond the traditional domains and regions of belonging. Other reasons for the levelling of the features of local variants also exist. [START_REF] Holes | Community, dialect and urbanization in the Arabic-speaking middle east[END_REF] describes the linguistic practices following the urbanisation process in three Arabic capital-cities: Manama, the capital of Bahrein, Amman, the capital of Jordan, and Baghdad, the capital of Iraq. All three case studies exhibit a prevalence of features of the smaller urban community variety over the Bedouin variety.

Regarding Bahrein, urbanisation and the consequent increased literacy resulted in level dialect differences. In Jordan, following the massive Palestinian immigration due to the Arab-Israeli wars, urbanisation was seen on a larger scale. Holes observed some general diastratic tendencies, such as different choices between young women and young men regarding whether they are Palestinian or Jordanian. The first group seems to be more inclined to follow phonetic patterns which are perceived as sophisticated (such as the phonetic [P] urban variant of */q/), while the second group followed masculine models (i.e. preferring the typical Bedouin [g] for */q/). Palestinian men in general tended to avoid Palestinian identity language markers, such as [P] or [k] by instead opting for [g] as an empathetic feed towards Jordanian interlocutors. 43 Another tendency highlighted by [START_REF] Holes | Community, dialect and urbanization in the Arabic-speaking middle east[END_REF] is the general substitution of the rural [č] (*/k/), charged with negative evaluation, with both the urban and Bedouin [k]. Baghdad also exhibits practices connected to the perception of one particular variety as more prestigious than the others. In that case of opposition between sedentary q@ltu-dialects and Bedouin g@let-dialects, this broadly corresponds to the speech of religious communities, where the Muslims are g@let speakers, while non-Muslims are q@ltu speakers. 44 In Baghdad, 43 See also the study developed by Abd-El-Jawad (1987) regarding the Jordan linguistic association with socio-political values relating to the issue of Palestine. He analysed new prestigious local or regional varieties in dichotomous competition with MSA in informal settings. 44 These names represent the different phonetic realisation of */q/ in the word q@lt, meaning 'I said'. In addition, in Tunisia, the speaker is aware of the two different way to speak, and they use tkall@m b-@l-qāla, as opposed to tkall@m b-@l-gāla, to distinguish between the /q/ and /g/ pronunciation. See chapter 4 for a deeper analysis of this phenomenon.

the Muslim-Bedouin dialect is the one which is currently perceived as more prestigious because of the political prominence of the Muslim population since Iraq's independence in 1932. In brief, simply in terms of diglossic realities, the process of koineisation of Neo-Arabic varieties has proven to be more complex than that which can be explained by a simple Fergusonian H-L dichotomy, because of contextual factors related to language use in various ways. However, in all the three cases reported by [START_REF] Holes | Community, dialect and urbanization in the Arabic-speaking middle east[END_REF], a trend seems to be emerging: the dialects of the capitals have lost the connotations they previously had, i.e. being linked to specific ethnic-religious communities, and have instead taken on the role of the country's koiné.

Considering the distinctive macro-linguistic-features of sedentary and Bedouin dialects, i.e. those already schematised in tables 1.1 and 1.2 of section 1.1.2, the levelling out of these characteristics can result in two possibilities:

1. A mix of the two varieties, or 2. The prevalence of one variety over the other.

According to [START_REF] Ghoul | Variations linguistiques dans le marquage du territoire dans la ville de tunis[END_REF], the urban koiné of Tunis is a variety of the Tunisian dialect, characterised by flexibility, inclusiveness and modularity, which Ghoul defines as la langue in vivo in comparison to la langue in vitro. 45 In other words, a kind of spontaneous language policy, based on individual initiative, that corresponds to the effective language practice of the speakers. Indeed, [START_REF] Ghoul | Variations linguistiques dans le marquage du territoire dans la ville de tunis[END_REF] states that: 'En raison de son caractère informel, cette langue paraît beaucoup moins contrainte que celle de l'affichage officiel, même si certains écrits peuvent parfaitement répondre aux exigences de l'écrit normé, notamment les enseignes de certains commerces ou de certains établissements scolaires privés.

La différence fondamentale entre la langue in vitro et la langue in vivo est que cette dernière introduit, en plus de l'arabe standard et du français, l'arabe dialectal.' 46

It is now the dialect of reference and has been assimilated into the 'Standard' Tunisian Arabic, which is now perceived as the prestigious variety of Tunisian (Mion, 45 Corresponding to a language policy decided by the State. 46 'Because of its informal nature, this language seems much less constrained than that of official signs, even if some written material can perfectly meet the requirements of standard writing, such as the signs of certain businesses or private schools. The fundamental difference between in vitro and in vivo language is that the latter introduces, in addition to Standard Arabic and French, dialectal Arabic.' My own translation.

2004; Gibson, 2008;[START_REF] Durand | Dialettologia araba[END_REF][START_REF] Tarquini | musica rap e convergenza linguistica in arabo tunisino[END_REF]. In particular, it has been proposed as a variety which should be elevated to a standard by a Tunisian association known as 'Derja', which was created in 2016, for the promotion of the Tunisian dialect and its recognition as an official language of the country. The militant members of the association are mainly university professors, who meet regularly at 'Beit el Bennani', a private house in the medina of Tunis, which has become a kind of cultural space. 47According to some, however, the banner of Tunisian koiné as the language of the people is nothing but a political strategy, devised by the left of Bouguibian heritage, for dividing the people, in which the koiné of the elite is chosen as the national language, at the expense of the internal and southern regions, which are less developed and less permeated with French culture and especially bilingualism. In a similar way, Allal and Geisser (2018) state that the langue-mixte-franco-tunisien is the language of power, not the language of the people, and that same would consistently be difficult for some people to understand as it is less involved in French biculturalism and bilingualism. In fact, [START_REF] Myers-Scotton | Carol myers-scotton: Multiple voices: An introduction to bilingualism[END_REF] defines the ability to code-switch back and forth between the foreign language and the local one as a mark of the educated elite and thus as a typical elite closure practice. The same practice has also been registered by [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF], as quoted above. Before presenting the dialect advocacy work of the Association Derja, it is however necessary to clarify a few questions about the concept of a national standard language.

Diffusion of spontaneous orthography

Standard Languages

'standard (n.) A term used in socio-linguistics to refer to a prestige VA-RIETY of a LANGUAGE used within a SPEECH COMMUNITY. 'Standard languages/dialects/varieties' cut across regional differences, providing a unified means of communication, and thus an institutionalised NORM which can be used in the mass media, in teaching the language to foreigners, and so on. Linguistic FORMS or DIALECTS which do not conform to this norm are then referred to as substandard or (with a less pejorative prefix) non-standard -though neither term is intended to suggest that other dialect forms 'lack standards' in any linguistic sense. The natural development of a standard language in a speech community (or an attempt by a community to impose one dialect as a standard) is known as standardisation' [START_REF] Crystal | A Dictionary of Linguistics and Phonetics[END_REF]. 48 Let us imagine a native Tunisian student who, for example, moves to the capital Tunis. Based on what we saw in the previous section, it is most likely that, in order to communicate with his classmates, he will gradually adopt more and more of the Tuniskoiné, without however ceasing to use his own dialect of origin for communication with his family. Similarly, let us imagine a blogger who does not want to address a particular reader, but instead wants to communicate with the whole of Tunisia by making an inclusive linguistic choice. He might choose to level the features of his own dialect, in favour of a kind of Middle Tunisian. However, if a second blogger, regardless of whether they are writing in the same period or not, makes the same kind of linguistic choice, it is not certain that the linguistic product would be the same as the first one. The fruit of the spontaneous choices of each speaker would be a singular product shaped by the variables of the specific context, and is perhaps not even an idiolect, since it would not necessarily be maintained over time, even by the same writer. These exemplified processes are rather spontaneous and are operated at a more or less conscious level, and are of the same level as that which Tunisians already use when they find themselves sharing spaces, whether real or virtual, which 48 Emboldened and capitalised words are taken directly from Crystal. are different from those of their origin. These are down-top processes, operated in order to facilitate inter-regional communication. This does not mean that Tunisian native speaker from different regions necessarily have great difficulties in terms of intercomprehension. Despite this, some Tunisians would like to propose the Tunisian dialect as the official language of the country. In particular, following the Arab revolutions, the question of standardisation of Tunisian has begun to be raised as a matter of identity prominence. In order to standardise Tunisian, as we have seen in the definition of standard, institutionalised norms are required to regulate its use. However, before considering norms, it is imperative to figure out what linguistic material to operate on.

It is from this point that at least two choices arise: 1. To elevate to a standard version of the koinai that we have seen emerging in the 20th century; 2. Levelling the traits of the various rising varieties within the country by creating a rather artificial seeming Middle Tunisian.

In the first case, it would be a choice which should be made on the basis of the most prestigious koiné or the most widespread through the national media, without taking for granted that same is probably the koiné of Tunis. In the second case, it would be a rather complicated operation because each individuals' perception of what the most representative features of the Tunisian identity are, which should be kept alive in the national language, so that it represents the whole nation. However, choosing either of the first type or of the second type, in favour of a national language, would certainly destroy local micro-identities, despite the fact that having a national standard does not necessarily presuppose the suppression of local varieties, which could still be maintained in private contexts or in contexts in which all the participants in the communication belong to the same linguistic micro-community. In any case, regardless of whether one wants to proceed with the first option or prefers the second, the first step is orthographic standardisation.

The officially recognized languages in Tunisia today are French and Standard Arabic, so at the level of standard orthographies, both the Arabic and Latin orthographies exist. On the, albeit infrequent, occasions when Tunisian Arabic is written, it has been encoded using the Arabic orthographic system, 49 with the exception of electronic 49 See [START_REF] Langone | Le journal de la médina. un récent projet éditorial en arabe tunisien[END_REF] for an example. communication and messaging contexts, where Tunisian is also written in Latin characters, as seen in section 1.3.1. However, the use of the Arabic orthographic system for the encoding of Tunisian presents a number of problems, which could be solved through the creation of a new encoding system modulated on an approximation of Tunisian phonology. It is probably also for this reason that the recognition of Tunisian is perceived by some as a replacement for Standard Arabic, which was a vehicle for a rich literary tradition, as well as a relative of the Classical Arabic holy language. The Association Derja proposed both an Arabic-based and a Latin-based input system for Tunisian encoding, as explained later in this chapter (section 1.2.2). At the same time, the encoding of Tunisian in Latin characters evokes a historical precedent, i.e. that of the Maltese language (the only Arabic dialect that reached the status of a language (Miller, 2012)). Azzopardi-Alexander and Borg (2013) report that the Arabic vernacular spoken in Malta is the result of linguistic contact with Muslim Sicily, but that its origin seems to have been Tunisia. In fact, Maltese exhibits some areal traits typical of Maghrebi Arabic, although it has diverged from Tunisian Neo-Arabic during the last eight hundred years of independent evolution. Both historical and ideological reasons, among others, have led to the orthographic standardisation of Maltese in Latin characters. 50 Let us then examine the motivations, implications, and the canonical path of the orthographic standardisation of a language.

Orthographic Standardisation

There are many reasons for supporting the use of a single regularised orthography for a country's language or dialects. The main reason is certainly the potential to generate a shared social and linguistic identity for communities that use different spoken varieties of a language. The use of a single orthography offers a way to overcome regional boundaries that represent a division not so much of territory, but more importantly of identity [START_REF] Simons | Principles of multidialectal orthography design[END_REF]. For example, in the case of the Chinese logographic system, the shared writing system has had the desired effect, quantitatively extending the possibility of reading and reducing the difficulties of learning a large number of Chinese characters. 51 Regardless of the advantages of a shared writing system, there are also potential difficulties, such as the issue of 'artificial systems' that arise during the construction of a multi-dialect orthography, which ends up representing none of the varieties that were intended to be synthesised. This is a risk particularly in divergent phonological systems where a shared orthography cannot faithfully adhere to the phoneme-grapheme correspondence of each variety which it seeks to represent.

According to [START_REF] Simons | Principles of multidialectal orthography design[END_REF], there are two basic options in this situation:

1. A specific dialect may be selected as the basis for the standard and used as a writing model, 2. Choices are made regarding the development of an orthography calibrated so that the set of its features is representative of the set of dialects sublimated into the variety which is elevated to the standard.

The resulting spelling will not represent the most psychologically real spelling for each individual dialect, but can still be easily learned and used for all of them. Simons argues for the second option in all circumstances, because the first requires speakers of non-standard dialects to learn at least some aspect of the orthography by rote memorisation. 52 In addition, there are proposals that aim to normalise not only spelling, but also grammar and vocabulary, such as Corraine's standardisation proposal for Sardinian, which included grammar, orthography and lexicon standardisation. In this case, the proposal was refused, as it was mostly based on Logudorese Sardinian, one of the two main varieties that the Sardianian's traditional classification takes into consideration, together with Campidanese Sardinian. As a result, language speakers claimed the right to be informed and involved in the decision-making process [START_REF] Lai | Language planning and language policy in sardinia[END_REF]. As also highlighted by [START_REF] Simons | Principles of multidialectal orthography design[END_REF], direct involvement of the people is in fact necessary for the development of a valid standard, because the most important factor for the success of language planning measures are their acceptance by the speakers of the language(s) involved. Regarding Sardinian, with its high level of linguistic variation, it would be 51 A logogram can be used in a number of languages to represent words with similar meanings. The same is true for alphabets, but the degree to which they can share identical representations for words with various pronunciations is much more limited. Arabic numerals are also logograms. 52 In order to show how an orthography can be created by drawing from multiple dialects, Simons discusses the Dani language of Irian Jaya (Western New Guinea), based on data in [START_REF] Bromley | The Phonology of Lower Grand Valley Dani[END_REF].

possible for one variety to gain ground on another if speakers gave it greater prestige [START_REF] Lai | Language planning and language policy in sardinia[END_REF]. One of the most common reasons for promoting a written standard is the desire for political or cultural unity in favor of the common development of the country. A single standardised writing system makes communication easier in a number of ways, such as in terms of education, given that instruction in Tunisia is taught in Tunisian, sometimes even in high school. Having a standard orthography can increase the functional domains of a language's use, which in turn increases its status within the community and reinforces community values [START_REF] Schiffman | Standardization or restandardization: the case for" standard" spoken tamil[END_REF]. Even though standardisation has certain indisputable benefits, it is not without social consequences.

One of the most obvious is the development of consciousness and beliefs about what is correct and incorrect. Prescriptive ratings of language forms are introduced with the written language form; native speakers tend to have smaller fixed notions of accuracy before a language is written. As such, an orthography represents a regulatory idea that does not have counterparts in the linguistic reality of the speech community [START_REF] Coulmas | Development of orthographies. Literacy. An International Handbook[END_REF]. The main concern about the standardisation process is that it actually leads to the disappearance of linguistic variation. Standardisation has been accused of potentially contributing to the loss of linguistic diversity, since a written standard inhibits the variability allowed in the language and thus inevitably causes the disappearance of features which diverge from the standard form of the language. In oral communication, a great array of dialectal variation can be maintained, but literacy necessarily favors standardised languages and discourages variation. Furthermore, standardised conventions for local languages are often explicitly shaped on the writing conventions of prestigious languages or languages of more widespread communication in order to facilitate the acquisition of the standard, which on the other hand may facilitate the loss of the original languages. Another reason why standardisation might contribute to the loss of variation lies in the status that is given to one variety over others as the former is elected as the standard. Writing certainly has this power to elevate the status of a language variety, to the detriment of other surrounding languages or varieties, which, not having a written tradition, may be perceived as lacking in prestige and therefore to be avoided, even in oral use [START_REF] Coulmas | Development of orthographies. Literacy. An International Handbook[END_REF]. However, the notion of standardisation in written languages is itself a convention, being an abstraction from spoken varieties [START_REF] Shortis | Gr8 txtpectations[END_REF]. This is particularly true for informal writing systems [START_REF] Thurlow | The language of text-messaging[END_REF].

Process of Orthographic Standardisation

Among all the factors that must be taken into consideration during the standardisation process, one of the most relevant is socio-political factors. In fact, there are often unspoken political implications in spelling choices that must be taken into consideration. Creating an orthography that is highly distinct from what is already perceived as the national writing system can also be seen as subversive or challenging to the goal of national political unity. Moreover, if a language is used in different countries, or different regions of the same country, it should always be coded with the same spelling system [START_REF] Baker | Developing ways of writing vernaculars: problems and solutions in a historical perspective[END_REF]. In addition to the political implications for countries that share the same language system, as mentioned in the previous subsection, one must also consider the consequences that the imposition of a spelling standard will have on linguistic variation in the target country. [START_REF] Grenoble | Saving languages: An introduction to language revitalization[END_REF] Many of these considerations are related to the perceptions of native speakers, and are insights that can only be held by members of one or more local communities.

Therefore, the ideal solution is for the standardisation committee(s) to consist of, or include, representatives from these local communities, so that they can be a part of the decision-making process. If this is not taken into account, committees may be inefficient, and may function differently than expected. The same is true with regard to particular cultural taboos. After determining which variety will be elevated to the national standard, members of the language planning committee should consider which aspects of spelling should be standardised. In addition to standardising the choice of writing systems and particular symbols within a system, it is also important to determine which other conventions should be introduced, such as whether, in the case of the Tunisian's Romanisation, capital letters should be included or not, since the Arabic alphabet does not have these, or how the punctuation system should be organised. In making these decisions, one should take into account the conventions used in the national and broader languages of communication, which in the case of Tunisia are Standard Arabic and French. The standard chosen may have to reflect one or both of these, or deviate from them in order to be accepted by the Tunisian language community. On the other hand, with reference to what we have seen in section 1.1.3, in the Tunisian diglossic and bilingual context, there are both standard languages and Tunisian Neo-Arabic, which [START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF] split into two types, i.e. national and local. Encoding a standardised Tunisian Neo-Arabic should not distort the state of functional organisation of this continuum. It should again be reiterated that this is a very delicate task, and that there are several issues to be taken into account when doing so. Finally, potentially the most delicate moment in the process of creating a standardised spelling comes in the testing phase. In fact, the spelling should be tested to evaluate its effectiveness and to address any problems. Depending on the accuracy of the work of the committee, a number of issues may arise, perhaps due to particular symbols or diacritics that may be ambiguous or difficult to use [START_REF] Grenoble | Saving languages: An introduction to language revitalization[END_REF].

A group that declares itself to be a promoter of the Tunisian dialect, known as the Association Derja, has already been mentioned above. The following section presents a brief overview of how they present themselves and the motivations that drive their work. my language and that of my grandparents... Dārža is our Tunisian language, that speaks for itself about our history. From Amazigh and Carthage we passed from the Romans and the Vandals to the Byzantines and the Arabs, our language is a sweet mix [containing] in it words and rules whose origins are Amazighi, Punic, Latin, Italian, French and Arabic... a very beautiful Tunisian mix explains the openness of our beautiful civilisation.

Association Derja

That language is the one that brings together the inhabitants of the country, it is the one that talks about its history, through it the culture of the country takes on value. In Tunisia, we speak our language with 12 million Tunisians, but until today it is still considered a dialect, it is not recognised as a language. For this reason, the Derja Association was founded in the year 2016 to show the value of our language and allow it to reach the place that it deserves. We will work on Tūnsi and encourage those who have worked on Tūnsi and those who are working on it, to reach our goal, so that it becomes written and recognised, with its own rules and dictionary. As it is said, a hand does not clap alone, so we want to gather all those who work and want to work on Tūnsi, and fix the process that makes it go from dialect to the language that the whole world can recognise, Tunisian language for all Tunisians.53 This is the Association Derja Manifesto, which appears on their website homepage. 54As we can see, the association has several goals: to codify Tūnsi, to develop a manual of the Tunisian language, and to create a library of works written in Tūnsi. Derja wants to be a place of debate and a catalyst for ideas. As per their manifesto, Tūnsi is perceived to be the true mother tongue of Tunisians. According to the members of the association, Tunisian dārža experiences the paradox of being the most widely used language, but not being recognised as a 'language' in its own right, as it does not exist as a means of written knowledge and information. In fact, few books, articles and other documents are published in this language, although Tunisian does appear in radio and television programs, and sometimes takes precedence over Modern Standard Arabic (MSA). The main reason that same has not already being recognised as the official language of Tunisia seems to lie in the absence of and lack of consensus on standardised spelling and written grammar. However, there have been many projects to promote Tunisian dārža in recent years, including various literary or artistic projects, for example, such as that pioneered by the writer Hager Ben Ammar, who has published traditional children's stories in Tunisian, which have been very successful commercially and were also a success, both culturally and educationally. Reporting the words of a member of the association, during a conference held on 27th February 2020: Dārža is the language in which Tunisians recognise themselves. It is one of the foundations of our identity, but in which we can also easily express ourselves.

[And] Today, in the context of democratic transition, our mother tongue is more than essential for social and cultural cohesion because of its origins in the collective popular memory of Tunisians. 55 Such is the message of the Association Derja, and the reason why the association wants to develop a manual of the Tunisian language and create a library of books in Tunisian.

55 My own translation.

The rise of spontaneous varieties

The process of the Tunisian dialect standardiation, if there will ever be one, will surely be long. However, in the meantime, there have been changes in Tunisian society in the last twenty years which have silently upset the linguistic balance of this country.

The invasion of social networks and instant messaging systems that now involve the whole world have taken hold. When discussing Tunisian, the immediate thoughts are of a Neo-Arabic variety, predominantly oral, being almost completely without a written tradition. As briefly mentioned earlier, there are some texts which are written in Tunisian, but at the same time, it has been at least twenty years since native Tunisian speakers started writing on social media, every day, in dārža. This is a huge change in linguistic habits, and is of such importance that it could justify a break from the tradition of defining Tunisian as 'predominantly oral'. It is also not the only manifestation of the exercise of writing in dialect. Of course, other examples can be seen in comics, the practice of writing graffiti on walls, advertising panels, blogs, forums and in general in Computer-Mediated Communication, which is the main means of expression of the so-called 'digital natives' and of their desire to communicate, via writing, in dārža.56 This is a very powerful identity signal. As such, it is necessary to analyse how this generation of cybernetic Arabs spontaneously organised themselves to write their own dialect.

In the age of the Internet and Computer-Mediated Communication (CMC), writing and literacy acquire new forms and functions with many implications for both language and culture, as well as for economics and politics. Indeed, another example of how non-state agents have made use of new communication technologies with far-reaching consequences for social and political events was the so-called Arab Springs of 2011, which is discussed in the next section (1.3). Regarding the vernacular writing practice, Auer (1997) connects it to the process of koineisation, the traits of which we have outlined in section 1.1.4. Auer defines this process as destandardisation, explaining that it consists of convergence of different regional vernacular features and a decrease in the simultaneous standard variety of the standard status flowing into larger-scale regional koinai. However, what exactly is meant when referring to the language of social networks? Section 1.1.1 documented how the Tunisian territory has been a land of passage for centuries and that the fruit of Arab invasions, despite being centuries apart, has led Tunisian to being a diverse Neo-Arabic variety, which can be divided into Bedouin and sedentary types of speech, as in section 1.1.2, with the corresponding macro-specifications. The presence of languages in relation to bilingualism was also explored, as well as the diglossic continuum between the two poles, i.e. acrolect and basilect, in section 1.1.3. In addition, the formation of urban koinai during the 20th century was also discussed, as well as how the koiné of Tunis is considered by some to be the national form of Tunisian, and the work that an association of amateurs speaking the Tunisian dialect is carrying out in order to promote the standardisation of its orthography. Corpus analysis will be used to try to answer the following questions:

1. What kind of Tunisian idioms should we expect to find on social networks? 2. Have users employed their own local variants, or have they levelled out overly local features for the sake of inter-comprehension?

Did users specifically choose an urban koiné?

It is first necessary to address a previous question: which spelling system have users spontaneously organised themselves into? This fascinating topic has attracted a lot of interest from linguists since the beginning of the practice of writing on early telephone messaging systems. Caubet (2017aCaubet ( ,b, 2018[START_REF] Caubet | Vers une littératie numérique pour la darija au maroc, une démarche collective[END_REF] points out that the practice of writing dārža in Latin script (Arabizi )57 has developed spontaneously, thanks to its users who have learned by themselves how to transpose it to the written word, as can be seen in section 1.3.2. Although unambiguous spelling rules for writing in Arabizi characters in dārža do not exist for any Neo-Arabic variety, the common and widespread practice of this new habit has indeed fostered the development of an informal process of orthography normalisation, where spelling variations do not prevent readers and writers from communicating effectively.

In any case, even if Arabizi were completely normalised, without presenting any ambiguity or variety, we could still not consider it to be a standard system, especially as it is not the result of a top-down process.

Arabizi instead represents a spontaneous process resulting from an implicit and unspoken agreement between participants in the conversation [START_REF] Miller | Contemporary dārija writings in morocco: ideology and practices[END_REF]. In this context, the parameters highlighted by [START_REF] Hary | The importance of the language continuum in Arabic multiglossia[END_REF], as discussed in section 1.1.3, are once again valid. In order yo confirm this without anticipating any issues in Computer-Mediated Communication, which we will address in section 1.3.1, reference should be made to the study of [START_REF] Riegert | Activists, individualists, and comics: The counterpublicness of lebanese blogs[END_REF] regarding one of the many linguistic phenomena present in Tūnsi, namely code-switching. Their study illustrates that the mechanisms of language choice, code-switching, and linguistic variation that occur on digital platforms are the same as those that occur in offline interactions.

Linguistic ideology practices presuppose that there is a well-established, recognised and institutionalised standard, but the concept of the standard itself is much more fluid in a multilingual context. This is why North African cities may represent more favourable contexts for the wide diffusion of these new solutions [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF].

However, the Tunisian linguistic reality seems so complicated to overcome the realm of 'youthful speech', as summarised by [START_REF] Léglise | Parlers jeunes ici et là-bas[END_REF], pp. 15: 'Ces « parlers jeunes » sont les parlers des jeunes urbains et sur ce plan, il reste à penser une théorie de la ville qui, au-delà de la vulgate, permette de saisir la sociabilité spécifiquement urbaine de telles parlures et de penser, en diachronie et en synchronie, les corrélations entre la gestion tendue des catégories socio-spatiales et la résolution ou non des conflits linguistiques, de la minoration des langues et des variétés, voire plus largement encore la covariance entre la production des espaces de ville et l'émergence de langues urbaines.' 58 58 'This « youth speech » is the speech of young urbanites, and on this level, a theory of the city remains to be thought of, which, beyond the vulgate, allows us to grasp the specifically urban sociability of such speech and to think, in terms of diachrony and synchrony, about the correlations between the tense management of socio-spatial categories and whether linguistic conflicts are resolved or not, the reduction of languages and varieties, and even more broadly, the covariance between the production of city spaces and the emergence of urban languages.' My own translation.

A rather debated question, regarding the spontaneous nationalisation of the Tunisian koiné, is if it is really representative of the whole Tunisia identity. Indeed, as can be seen in the next sections, the urban koiné of Tunis is becoming increasingly popular among the Tunisian media in the name of national identity assertion. However, as we already had occasion to note, Tūnsi, due to its mixed composition with French and as the language of an elite social class and developed regions, does not seem to adequately represent the middle social strata of the central and southern regions of the country.

The risk is therefore linguistic discrimination against the majority of the population.

New Spaces for New Communication

The conclusion of the previous section included an introducion of the expression parler jeune, which 'renvoie par effet de miroir à l'idée de résistance à l'ordre adulte ou politique dominant.'59

This adult order, can metaphorically represent the established order, as well as disappointing politics, a society that needs to be renovated, and a non-inclusive linguistic ideology. In relation to these elements, therefore, young people may not be the only ones who are young at birth, but they are the driving force behind rebelling against the order of a society within which they cannot see themselves reflected, in which they cannot find a space where they feel to belong. A widespread practice of contestation is that of rap or hip-hop, where discursive practices related to society are set to music.

The themes are in fact always those of social discomfort, such as violence, drugs, and social inequality. The first Tunisian youth radio is Mosaïque FM, where a mixture of the French and Arabic languages are used (Allal and Geisser, 2018;[START_REF] Kallel | Choix langagiers sur la radio mosaïque fm. dispositifs d'invisibilité et de normalisation sociales[END_REF]. TV series which are translated into or produced in Tunisian have begun to appear. One of the most famous, broadcasted during Ramad . ān, is Bolice, meaning 'police'. Produced by Underground Skills and directed by Majdi Smiri (/Maždi @s-Smīri/), this series is a parody of an American crime series that would have been unthinkable to produce before the revolution. Regarding series which have been translated into Tunisian, most of these are Turkish. The first was , /qlūb @r-rummān/, 'Pomegranate Seeds', translated in Tūnsi. As Miller (2012) points out, both satellite TV and the Internet have contributed to the great linguistic changes in the countries across North Africa in the years 1990-2000. In fact, previously, TV and radios were considered national tools for spreading Standard Arabic, cartoons were still in MSA, and TV series were not dubbed into Tunisian. However, MSA, according to Miller, was not flexible enough to adapt to the demands of a modern Arab world with the speed that this change required. However, in recent decades, globalisation has created a new neutral space for discussion. New technologies, starting with SMS-type messaging, allow for a faster, wider and more accessible 'social connection'. Speed does not only refer to the speed of spreading a message, but also to the ability of the technological system to adapt to the innovative impulses provided by the user. These characteristics mean that social networks mediated by technology wield such potential power that hindering them becomes virtually impossible. 60

The most obvious demonstration of what has just been stated is the wave of rebellions and demonstrations that began in the Arab world at the end of December 2010, starting in Tunisia. This wave relied so heavily on Internet communications and social networking tools, which are said to have contributed to these uprisings on two distinct levels:

1. Mobilisation, 2. Deconstruction.

Regarding the first case, digital platforms had a political impact, being instrumental in mobilising protesters and coordinating their activities. Indeed, young Internet-savvy people used social networking sites to disseminate information and organise rallies, repeatedly circumventing government attempts to shut down websites and servers. 61

Regarding the second level, the way in which Internet communication impacted the socalled 'Arab Springs' was the release by WikiLeaks of a series of protected information that deconstructed the image of Arab leaders, revealing their diplomatic agreements with the United States. Reposted across other types of media platforms, these revelations fueled resentment against Arab governments for the masses.

The suicide of Bouazizi in December 2010, is generally considered to be the casus belli of the Arab revolutions, but the truth is that the discontent had already been rife for some time. In 2008, due to a rigged public contest, a revolt that is considered to have been the forerunner to that of 2010 broke out in the town of Redeyef (Gafs . a). Despite the fact that the clashes between the striking miners and the army were very violent, almost no news reports about the incident were leaked. There are those who argue that 60 Another example of flexibility, as in section 1.3.2, is that demonstrated by their ability to adapt to complex linguistic realities, such as that of Tunisian.

61 Particularly in the uprising that began on 25 January 2011 in Egypt.

the Gafsa uprising had much more potential than that of 2010, but that its retreat was also determined by the low resonance of same across the country at the time, due to the embryonic stage of the media. In Tunisia, until 2011, several sharing platforms such as Youtube and Flickr, various Facebook and Twitter pages, specific blogs and information sites, including the Wikipedia page on censorship in Tunisia, were blacked out. As far back as in the 90's, some pioneer 'cyberactivists' were fighting censorship through counter-information activities, among them Anonymus and Takriz. 62 In May 2010, there was even a campaign against web censorship known as sayy@b s . ālaè. 63 These facts were further fuelling a groundswell of discontent. As shown by the chronology of events reported by [START_REF] Bettaïeb | Dégage: la révolution tunisienne[END_REF], the revolution's outbreak was strongly supported by media outreach. During the months following Bouazizi's suicide (on 17th December 2010), the protests quickly spread to the neighbouring city of Sidi Bouzid. The news spread just as rapidly, through both photos and hashtags, such as #sidibouzid, spread on social networks, through which the demonstrations were organised in the squares.

Moreover, cyberactivists spread the images to satellite channels such as Aljazeera and

France24.

Many Tunisians today describe the revolution as a failed revolution, mainly because of the serious economic and political crisis that has been going on for ten years. The great conquest of the revolutions was freedom of expression, and this seemed to be exactly the way in which to express all of one's frustrations. The main channel for such expressions have been, since the beginning, the social networks.

Regarding Ben Ali's last speech (13th January), every time he had previously made a speech, he had always read prepared notes in fus . èā and pronounced them respecting the phonetics of the prestigious variety. 64 On the other hand, Boussofara (2006) argues that Ben Ali, by imposing fus . èā as the only language of official political discourse, made a calculated choice aimed at legitimising his authority and breaking away from past traditions. The implicit devaluation of dārža on the official level, however, never 62 There was the particularly sad case of a well-known Takriz activist Zouhair Yahyaoui, creator of the online magazine TUNeZINE, who was sentenced to two years in prison for spreading false news and fraudulent use of the media.

63 Note the Arabizi spelling used for the slogan Sayeb Sala7, as reported by the article published on Nawat blog at the following link: https://nawaat.org/2010/05/29/ anti-censorship-movement-in-tunisia-creativity-courage-and-hope/.

Consulted on 4th April 2021.

64 Unlike his predecessor Bourguiba, who did not disdain the use of dārža or French. As Sayahi (2014) reports, Bourguiba often expressed himself in 'Middle Arabic', making use of frequent codeswitching from one language to another.

prevented the latter from conquering more and more areas of action according to the previously mentioned trends. The linguistic issue was immediately highlighted as being politically prominent with the first post-dictatorship Republic President, Moncef Marzouki, 65 who considered the Arabic language to be the main means of pan-Arabic identity and inter-communication. He rejected the practice of introducing the French lexicon into Tunisian, considering this mixed language to be a political expression of a specific social group, i.e. the decadent elite of Tunis, that would marginalise the internal regions of Tunisia. However, his successor, President Beji Caid Essebsi, expressed himself in Tūnsi, supporting the idea that the language of the urban elite of Tunis coincides with the language of the people (Allal and Geisser, 2018). A new linguistic inversion was represented by the President Kaïs Saïed, who has shown a preference for what, as in section 1.1.3, has been defined as LA, or the language used in the literary heritage. The President's choice to express himself in LA during public speech has been largely criticised by the online Tunisian journal webdo.tn: 'Qu'il se mette à la place des nombreux Tunisiens qui attendaient son allocution et ont été frustrés de ne pas comprendre ce que disait leur président.' 66 However, the situation regarding diglossia and bilingualism in Tunisia has already been discussed (section 1.1.3), and, on the contrary, we have seen that perhaps it is the socalled langue mixte that is not widely understood by the Tunisian people outside of the area under the influence of Tūnsi.

65 Historical opponent of Ben Ali. Exponent of the Islamic party Ennahda. 66 'Let him put himself in the shoes of the many Tunisians who were waiting for his speech and were frustrated by being unable to understand what their president was saying.' My own translation. Article available at: https://www.webdo.tn/2020/03/21/ et-la-derja-tunisienne-pourquoi-kais-saied-senferme-t-il-dans-cette-langue-rigide/. Consulted on 4th April 2021.

Tunisian Computer-Mediated Communication

Intentional communication is narrowly defined as any action or actions that a person consciously uses to affect another person's behaviours [START_REF] Miller | On defining communication: another stab[END_REF], while Computer-Mediated Communication (CMC) is a branch of study that deals with how information technologies, together with computers, make peculiar forms of communication possible between people at a distance from one another (Miller, 2012). As early as 1992, Walther and Burgoon (1992) stated that: 'Computer-Mediated Communication is no longer a novelty but a communication channel through which much of our business and social interaction takes place, and this transformation is expected to continue.' One new phenomenon is the ability of human beings to adapt their language to the medium of communication. With the advent of CMC before web applications, such as personal e-mails, chat channels and forums, a new type of discourse was born, which is defined as the Electronic or Computer-Mediated Discourse or Digital Networked Writing (DNW). This is known to be a type of writing characterised by linguistic and extra-linguistic peculiarities specific to technological networks [START_REF] Panckhurst | Le discours électronique médié: bilan et perspectives[END_REF]Androutsopoulos, 2011;[START_REF] Panckhurst | Short message service (sms): typologie et problématiques futures[END_REF][START_REF] Crystal | Language and the Internet[END_REF]. Messaging and the use of social networks has become the emblem of a globalised society. DNW has distinctive characteristics that differs from in-person oral communication. As [START_REF] Morel | Les 'textos' plurilingues: l'alternance codique comme ressource d'affiliation à une communauté globalisée[END_REF] 67 'In oral conversation, people hear each other, perceive timbre and intonation, and often also gestures, facial expressions and movements. They can react quickly, interrupt the interlocutor, integrate or refuse other interlocutors, and so on. In communication via SMS or WhatsApp, all of this is quite different. Some of the most salient features of cell phone mediated communication include the use of written code, a time-delayed regime with a variable time frame and a spatial arrangement where the physical absence of the other is dominant.' My own translation.

It is spontaneous;

4. It is interaction-oriented.

There are many scholars, who have, in recent decades, devoted themselves to the study of messaging and communication via social networks. For a detailed analysis of the characteristics of CMC and DNW, it is imperative to refer to [START_REF] Crystal | Language and the Internet[END_REF]; [START_REF] Baron | Always on: Language in an online and mobile world[END_REF][START_REF] Baron | Computer mediated communication as a force in language change[END_REF]; [START_REF] Panckhurst | Le discours électronique médié: bilan et perspectives[END_REF][START_REF] Panckhurst | Short message service (sms): typologie et problématiques futures[END_REF]; [START_REF] Herring | Gender and (a)nonymity in computermediated communication. The handbook of language, gender, and sexuality[END_REF]Androutsopoulos (2011). With regard to the Andoutsopoulos, he argues that DNW has the potential to expand the vernacular writing practice, in addition to diversifying writing styles and setting up new writing norms. He states that: '[t]he expansion of digital literacy practices afford vernacular written usage more space, visibility and status than ever before, and vernacular usage itself is diversified in what we might call 'old vernaculars', representing locally bound ways of speaking that traditionally didn't find their way into (public) writing, and 'new vernaculars' -new patterns of differentiation from written standards, indexing practices and networks of digital culture.'

This statement seems very much in line with the view of [START_REF] Ong | Oralità e scrittura (Original Title: Orality and Literacy. The Technologizing of The Word)[END_REF], who describes the potential of writing to entrust the word to space by expanding its power. Indeed, Androutsopoulos (2011) coined the expression 'secondary orality' to depict DNW, employing three keywords to portray the essence of DNW: orality, compensation and economy. With regard to the second of these, same refers to the compensatory-devices employed in DNW to offset the lack of prosody, due to non-present communication, which will be further explored in the following section. Concerning the last theme, same refers to the strategic apparatus for achieving both effective and concise communication. These key words also fit the context of Arab DNW. One of the first to analyse the Arab CMC landscape was [START_REF] Mimouna | Is english there?: Investigating language use among young algerian users of internet[END_REF], who dedicated herself to the study of Algerian users of e-mail communications. The initially concern was the study of the different linguistic features of e-mail communication in order to discover the various attitudes towards the impact of the e-mail language on the standards of the traditional written language. She also offers a pedagogical dimension to the study of e-mail communication among young university students. Among these early scholars of Arabic CMC was [START_REF] Albirini | Modern Arabic sociolinguistics: Diglossia, variation, codeswitching, attitudes and identity[END_REF], who offers a broad vision on the uses of Arabic on the Internet from a socio-linguistic perspective. In his analysis, the socio-linguistic landscape of digital media is characterised by multilingualism and the widespread use of code-switching. In particular, it shows that young network users are not monolingual and that they are aware of the communicative context in which they interact.

For this reason, they use an informal register that does not aim to reproduce the rules of standard language. In comparison, [START_REF] Palfreyman | a funky language for teenzz to use:" representing gulf Arabic in instant messaging[END_REF] focused on Gulf Arabic instant messaging, while [START_REF] Warschauer | Language choice online: Globalization and identity in egypt[END_REF] focused on Egyptian CMC, analysing it in terms of broader global trends of language, identity, and globalisation.

With regards to the Tunisian situation, an explanation has already been provided regarding the fact that since the revolution there has been a multitude of opportunities, which were previously almost completely absent, for informal public political communication, such as press conferences, interviews and televised political debates.

The language of public political debate, therefore, has given up more and more ground to dārža [START_REF] Sayahi | Diglossia and language contact: Language variation and change in North Africa[END_REF]. Since the revolution, the rise of dārža in public Maghrebi spaces, both virtual and real, is also due to the pride in one's 'Tunisian-ness', which arose after Tunisia initiated the uprisings in the Arab world: if fus . èā is considered to be the 'real' Arab language, dārža is the symbol of the Tunisian national identity as opposed to the rest of the Arab world [START_REF] Sayahi | Diglossia and language contact: Language variation and change in North Africa[END_REF]. [START_REF] Caubet | New Elaborate Written Forms in Darija: Blogging, Posting and Slamming in Morocco[END_REF] reports that a Moroccan blogger called Mohamed Sokrate, who used to write in MSA, decided, after being released from prison detention, to start writing in dārža in 2014, explaining this choice as a way to get closer to all Moroccans. However, there are also different attitudes, such as that of young people who intentionally write long posts on Facebook in fus . èā and in Arabic characters. [START_REF] Thurlow | From statistical panic to moral panic: The metadiscursive construction and popular exaggeration of new media language in the print media[END_REF] also reports the attitude of 'language puritans', who see texting practices as having a negative influence on standard writing. This all forms part of the identity debate that has exploded in the wake of the revolution, and in this sense, depending on one's points of view, even the use of fus . èā can be a way to regain possession of part of one's own identity, i.e. the Arab part. However, dārža not only represents this sense of identity, but is also a way to encourage the diffusion of culture, making sure that high culture does not necessarily mean the use of French, as expressed in the Association Derja presentation in section 1.2.2.68 Laroussi (2010) makes an interesting observation about the Maghreb language policies, saying that these never corresponded to reality, because there is no democracy in public life and the process of language management was not based on the consensus of social groups, dialogue and inclusion. Today, perhaps, this change is finally starting to take place. Therefore, in the case of Tunisia, social change consists of both the revolution and the advent of the Internet and social networks, two phenomena that are seen to be strongly interconnected. Both phenomena are an engine for linguistic change, and in this regard, [START_REF] Crystal | Language and the Internet[END_REF] states that: '[T]he differing expectations, interests, and abilities of users, the rapid changes in computer technology and availability, and the rate at which language change seems to be taking place across the Internet (much faster than at any previous time in linguistic history) means that it is difficult to be definitive about the variety's characteristics.'

Similarly, [START_REF] Heath | Orthography in social media: Pragmatic and prosodic interpretations of caps lock[END_REF], speaking about English orthography on social media, argues that:

'With the popularisation of social media and the sheer number of users participating in online conversation daily, social media has become a mecca of rapid language change and standardisation.'

This phenomenon can also be seen in Tunisian Neo-Arabic, where traditionally spoken language varieties are being written in similar ways because they are used in online spaces. Moreover, this is also a mass daily practice. [START_REF] Crystal | Refining stylistic discourse categories. English linguistics in honour of Magnus Ljung[END_REF] had already posed the question of how to categorise this new type of communication featuring hybridised text including an informal style of speech combined with features of written texts. [START_REF] Baron | Letters by phone or speech by other means: The linguistics of email[END_REF], for example, consider e-mail writing to be a creolising blend of written and spoken, being the product of new linguistic modalities. Androutsopoulos and Schmidt (2002); Androutsopoulos (2011); [START_REF] Jaffe | Orthography as social action: Scripts, spelling, identity and power[END_REF], when discussing Greeklish, which is the Greek representation in Latin script employed in CMC, use the term 'neography', which had already been coined by the French linguist Jacques [START_REF] Anis | Neography: Unconventional spelling in french sms text messages[END_REF].

This hybridisation can also be seen in the way that Arabizi tends to mirror Tunisian Neo-Arabic phonology by its approximation, while also including features, such as numbers, selected by analogy with the graphemes of the Arabic alphabet. Regarding this particular dārža encoding, i.e. the Arabizi system, [START_REF] Younes | A quantitative view of Tunsian dialect electronic writing[END_REF] collected a corpus of 85,000 Tunisian messages (approximately 37,000 were Facebook posts). 43,222 messages were in dārža or written in Arabizi, equating to more then the half. Regarding Facebook posts, 81% were written in Arabizi. It goes without saying that this spelling system needs to be a part of the subject of this study, and as such, the next section being dedicated to it.

Digraphia

The term digraphia is used to refer to the use of two different graphical systems to encode the same language. Androutsopoulos (2012) explains that he addresses the greeklish phenomenon through both an autonomous and ideological approach, specifying that an autonomous approach sees orthography as a neutral technology for the representation of spoken language, in contrast with the so-called ideological approach that views orthography as a set of social practices in specific social and cultural contexts. Besides orthographic choices for specific symbols, script choices can also be motivated by identity distinctions [START_REF] Grenoble | Saving languages: An introduction to language revitalization[END_REF][START_REF] Miller | Contemporary dārija writings in morocco: ideology and practices[END_REF]. 69 The invention of writing, despite occurring relatively recently in the history of the human species, revolutionised the way language can be used. As introduced by the linguist Ferdinand de Saussure in the early days of modern linguistics, a linguistic sign is a link between a concept (signified, from French signifié) and a sound pattern (signifier, from French signifiant), which is based on socio-cultural arbitrary conventions, and while it is perhaps impossible to deliberately change the laws of sound, other aspects of language are open to deliberate modification and innovation. One must also consider the well-known distinction between semasiographic, representing ideas, i.e. the mathematics language, and glottographic systems, representing elements of a specific spoken language (Sampson, 1985, 26-45). 70 Finally, within the category of glottographic writing systems we can identify logographic (i.e. based on morphemic or polymorphemic units, such as words) and phonographic writing systems and/or strategies (that reproduce a phonic segment, e.g. a phoneme). In brief, writing is a social practice and a mode of communication in its own right [START_REF] Coulmas | Writing and society: An introduction[END_REF]. Linguistic behavior is in all circumstances a matter of choice, just like the role one takes within a specific situation.

As such, it is necessary to take the power of written language as a political tool into consideration, for example, the Rosetta Stone 71 or the Code of Hammurabi, the former of which contained a Ptolemaic decree and the latter a series of dispositions of the king of Babylon.

69 For example, [START_REF] Coulmas | Development of orthographies. Literacy. An International Handbook[END_REF] points to the fact that so many groups have developed their own scripts, syllabaries in particular, as evidence of the importance of a script as a marker of identity.

70 Among the semasiographic systems we can consider also the programming languages.

71 Interestingly, the Rosetta Stone shows an inscription in three different scripts: hieroglyphics, demotic and ancient Greek. The first and second of these are two different encodings of the same language, with ancient Egyptian that was encoded in hieroglyphics being limited to priestly functions or inscriptions, while the demotic was more widely understood and used.

Both codes are, moreover, engraved on stone, a further affirmation of the strength and power of the written code in a socio-political context, and the object of reappropriation by the masses in modern times through the practice of graffiti on walls, including public expression of political opinions and criticisms of society, normally prohibited by law (Akbar, 2019). The practice of writing on walls, in the globalised era, is flanked by writing on a type of virtual wall, the visibility of which assumes an unquestionably greater scope, that is, the Facebook wall, which is referred to in English as 'wall', in Spanish as 'muro', in French as 'mur', and in Arabic as , 'wall table' or 'wall frame'. 72 Another important factor to note is that writing produces changes in both language and society, making, among other things, diachronic variation visible and thus bringing it to the attention of researchers [START_REF] Coulmas | Writing and society: An introduction[END_REF] and to the sight of the native speaker himself, who, by observing his own product, has more opportunity to develop linguistic thoughts, which is a preliminary and indispensable step for the transition to the orthographic standardisation of a system which was previously only oral (Mion, 2017a). In fact, it is no coincidence that in recent years, in the context of Arabic Natural Language Processing (ANLP), Tunisian has acquired a continually increasing visibility. Indeed, the enhanced availability of written texts has made it easier to collect data for linguistic analysis. 73 As seen above, even in the choices made by the Association Derja, there are two spontaneous encoding possibilities for Tunisian; one in Arabic characters and one in Latin characters. This phenomenon is also known in other languages, the orthographic systems of which are not Latinbased, such as Greek (Greeklish, (Androutsopoulos and Schmidt, 2002)) and Serbian (Latinica instead of the Cyrillic alphabet). However, in terms of Tunisian, neither of these systems was created for encoding Tunisian, and the first was in fact created 72 As with the practice of graffiti, the use of Facebook can also be subject to control, either among peers or from above, especially in dictatorial contexts. Regarding the latter, sometimes control is 'limited' to the prohibition of expressing an opinion contrary to that of the dictator, as in pre-revolution Tunisia. In other contexts, Facebook is downright illegal, as in the People's Republic of China.

73 [START_REF] Younes | A quantitative view of Tunsian dialect electronic writing[END_REF] showed that the Facebook usage rate in Tunisia was around 97%. YouTube monopolised second position (1.3%), while Twitter took third (1.01%).

for encoding Arabic. Consequently, graphically representing Tunisian Neo-Arabic with either system presents some difficulties. The most obvious difficulty in using an Arabiclike spelling for Tunisian is that of code-switching, where two possibilities are given to the native speaker:

1. Change the input system only for code-switching elements; 2. Encode code-switching elements in Arabic characters.

The first choice presupposes a type of challenge for the author of the text. In fact, anyone who has ever written even a single Arabic word in electronic text that involved both writing systems knows that the formatting of the text quickly goes out of control.

The second possibility instead represents a reality which is actually practiced through the approximate transliteration of foreign words into Arabic characters.

This phenomenon opens the doors to curious hybrid forms (henceforth hybridisms) due to morphological integration of a lexical transfer (following [START_REF] Regis | Appunti grammaticali sull'enunciazione mistilingue[END_REF]

point of view)

or to mechanisms oriented towards a loan adaptation, that [START_REF] Poplack | Introduction: How languages fit together in codemixing[END_REF] define as nonce borrowing. Hybridisms are words made up of a lexical morpheme from an X language and an inflectional morpheme from a Y language (Regis, 2010, 622).

As an example, the French word 'restaurants' can adapt to the morphological rules of Tunisian becoming , /r@stūrānāt/, with the Tunisian plural feminine suffix.

However, for some words, the understanding may not be immediate. The mechanism behind these formations, and their definition, is subject of an open debate, that can see this phenomenon as coming close to the phenomena of code-switching (CS) or loan adaptations, without however coinciding completely with either. In fact, unlike Myers-Scotton (1993), 74 Cerruti and Regis (2005) and [START_REF] Berruto | Fondamenti di sociolinguistica[END_REF] consider that it is better to keep hybridisms separate from CS phenomena, involving only the superficial linguistic system (i.e. words, morphemes and phonemes) and not the discourse, 1.3. New Spaces for New Communication although they are non-institutionalised manifestations of contact in use as much as CS (Cerruti and Regis, 2005, 193-194).

Another of the reasons that favors the input system in Latin characters instead is that, for reasons that will become apparent in the next section, native Arabic speakers are faster at writing in Latin characters (at least on computer keyboards). This consideration is supported by the emergence of Arabizi-based text entry support systems.75

These systems are still used today, despite the fact that there are now highly developed Arabic character input systems.

Definitions for CMC-related spontaneous spelling systems, such as Greeklish and Arabizi, have been proposed by scholars, among which, as reported by Androutsopoulos ( 2011), there is neography, which was coined by the French linguist Jacques [START_REF] Anis | Neography: Unconventional spelling in french sms text messages[END_REF], graphostylistics and respelling. The following section examines Arabizi in this context.

Arabizi encoding

The phenomenon of written Arabizi was born spontaneously at the end of the 1990s, following the arrival of the first electronic devices, to compensate for the lack of Arabic keyboards or input systems that allowed typing in Arabic characters. The name Arabizi seems today to be the most popular, together with 'Arabish', which comes from a mixture of the words 'Arabic' and 'English', which is the second most popular [START_REF] Bianchi | Arab english: The case of 3arabizi/arabish on mahjoob. com[END_REF]. The former probably comes from the word 'Arabic' and the Arabic word for 'English': /Pinglīzī/ as reported by [START_REF] Alghamdi | Arabizi in saudi arabia: A deviant form of language or simply a form of expression[END_REF], or from the merge of the words 'Arabic' and 'easy' as suggested by [START_REF] Caubet | Vers une littératie numérique pour la darija au maroc, une démarche collective[END_REF], who also mentions the names 'e-darija', '3aransiya', 'franco' or 'franco-arabe' for Moroccan Arabizi, which are mixtures of the Arabizi encoding for the 'Arabic' and 'French' languages: '3arabiya' and 'faransiya' [START_REF] Caubet | New Elaborate Written Forms in Darija: Blogging, Posting and Slamming in Morocco[END_REF][START_REF] Caubet | Apparition massive de la darija à l'écrit à partir de 2008-2009: sur le papier ou sur la toile: quelle graphie? quelles régularités? De los manuscritos medievales a internet: la presencia del árabe vernáculo en las fuentes escritas[END_REF][START_REF] Yaghan | Arabizi": A contemporary style of Arabic slang[END_REF][START_REF] Alghamdi | Arabizi in saudi arabia: A deviant form of language or simply a form of expression[END_REF]. Caubet retraces the progressive and massive passage from dārža to the written 'Do It Yourself' practice, which she describes as:

'une action spontanée et collective [...] d'acquisition de la lecture et de l'écriture d'une langue non-codifiée (Caubet, 2019, p. 391).'76

The name Arabizi was mostly used in eastern Arab countries, but in the wake of the release of the Arabizi movie, directed by Dalia Al-Kury, in 2006, became widespread everywhere. Recently, [START_REF] Fourati | Tunizi: a Tunsian arabizi sentiment analysis dataset[END_REF] proposed a specific name for Tunisian Arabizi, i.e. Tunizi, coinciding with the dataset they collected for Tunisian Arabizi sentiment analysis. There have been a few stigmatisers of Arabizi encoding, with some considering it to be a deviant type of encoding that could compromise expertise in Arabic-character encoding [START_REF] Alghamdi | Arabizi in saudi arabia: A deviant form of language or simply a form of expression[END_REF]. A number of tools have also In conclusion, the data shows that the preferred solution for Tunisian nowadays is still the Arabizi system, which is considered to be a neography of Tunisian, that can be isolated in diamesia. Regarding the transliteration of terminology, [START_REF] Gorgis | Transliterating Arabic: The nuisance of conversion between romanisation and transcription schemes[END_REF] distinguishes the romanisation from latinisation stating that transliteration, as the conversion of one writing system into another, is a generic term, while romanisation is more specific, being the representation of a writing language system in a Romance-language script. However, in contrast, latinisation also concerns the use of Latin vocabulary.

Concerning transcription in the Roman system, the source language pronunciation is represented by finding the closest equivalent in the Roman system, while Arabic romanisation is more a transcription process considering that, for example, short vowels are also encoded. 81 This is particularly true in terms of Neo-Arabic romanisation, which is the encoding of oral systems into Roman-based conventional encoding. As such, we will henceforth use the term romanisation to refer to the Arabic-romanisation system. Finally, Arabizi seems to be a process which falls somewhere between romanisation and creative transliteration, basically being a non-standard encoding of Tunisian phones through Latin graphemes, where for Tunisian phones which do not match the Roman 79 It would be worth investigating the motivations behind this sociolinguistic connotation of the Arabizi script. Certainly, the positive evaluation associated with this writing system is related to the youth environment, but it would be interesting to investigate the specific implications. 80 The same tendency is also confirmed by [START_REF] Caubet | Vers une littératie numérique pour la darija au maroc, une démarche collective[END_REF] with regards to Moroccan society.

81 When in Arabic script, short vowels correspond to diacritics.

alphabet, the substitution is graphical-analogical, i.e. a graphemic substitution based on an iconic similarity between Arabic letters and Arabic numbers (such as '7' for the Tunisian phoneme /è/). In some infrequent cases, we can see complex graphemic substitutions, such as @ employed in the word 'bouss@', būsāt, 'kisses', where the at sign replaces the feminineplural-ending '-āt', based on the analogy between the English name of the symbol @ and the phonetic realisation of the morpheme '-āt'. Tunisian Arabizi is therefore a spontaneous encoding, which operates according to some more or less shared principles that involve the use of French spelling where possible, such as in ch for , or ou for and ss for [START_REF] Caubet | New Elaborate Written Forms in Darija: Blogging, Posting and Slamming in Morocco[END_REF]Akbar, 2019).

Some preliminary remarks

In order to start gaining insight into the Arabizi encoding system, we must first examine some of its specific traits, which will then be the subject of a more detailed study in chapter 4 through a quantitative analysis of the corpus data.

Representation of Arabizi vowels

Tunisian Neo-Arabic presents a wide variety of vowel phones, which are usually ascribed to the phonemes of the tripartite system of Arabic /a/, /i/, /u/ and the corresponding long ones (Gibson, 2008). However, with regards to short vowels, further studies have take into account the possibility that Tunisian is a mixed Bedouin-sedentary type, considering the initial phonological distribution, namely the pre-hilalic /@/ < *a i ̸ = /u/ < *u, which was subsequently exposed to a process of partial re-phonologisation under the Arabic tripartite system. 83

These considerations are supported by the presence in Tunisian Neo-Arabic of both the convergence of /i u/ in [@] in opposition to /a/ [START_REF] Caubet | Propositions concernant la notation usuelle de l'arabe maghrébin: graphie arabe et graphie latine[END_REF], which is a Bedouin trait, and the sedentary convergence of /a i/ in [@], in opposition to /u/ [START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF](Durand, , 2012;;Mion, 2008b). Regarding long vowels, these do not undergo any major change, except in atonal syllables. Moreover, the general rule is that final long vowels, in an atonal syllable, are shortened, while the length is maintained for vowels in a tonic syllable. 84

One of the main differences between the representation of Tunisian in Arabizi and in Arabic characters lies in the encoding of short vowels, since the latter provides only three diacritics which are rarely used in informal writing. Regarding long vowels, these are better encoded in Arabic-script, even if with a certain inconsistency, as also recorded by [START_REF] Caubet | Vers une littératie numérique pour la darija au maroc, une démarche collective[END_REF] in the case of Moroccan. In contrast, in 83 The neutralisation of short vowels is the process of short vowels gradual flattening through their confluence in schwa [@]. This phenomenon is characteristic of Maghrebi dialects, especially the Moroccan dialect (Durand, 2012). 84 The length is restored to abbreviated long final vowels if a suffix is added to the word.

Arabizi, same are occasionally encoded through the repetition of the grapheme, for example, aa, which can encode both /ā/ and /Q/. Previous studies on Arabizi systems of other Arabic dialects presented an inconsistent representation of the vowel system, such as the study on the Kuwaiti dialect developed by Akbar (2019). Akbar also signaled a tendency to delete unstressed vowels, which is also found in the Tunisian system. Some phenomena that are instead visible through encoding in Arabizi, unlike the encoding in Arabic characters, are as follows:

(a) Palatalisation.

(b) Vowel metathesis.

(c) Monophthongisation.

The first of these is intended to be the Pimāla, namely the shift from /a/ ∼ /ā/ towards /i/ ∼ /ī/, in a not-marked context. The Tunisian /ā/ with Pimāla sounds like [ae: ∼ e: ∼ E:], which is the same for /a/, except for the length. where same is particularly heard, being both long and tonic), or strong (šadīda), heavily tending towards /i/ or more rarely with the Pimāla breaking in the diphthong ie. 86 This phenomenon is partially evident in Tunisian encoded in Arabizi, considering that palatalised /a/ is encoded through the grapheme 'e', with or without an accent, instead of 'a'. However, unless palatalised /a/ is encoded through a specific grapheme, it is impossible to determine its degree of intensity [START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF]Mion, 2008b). In example 1 below, it could be assumed that the grapheme 'è' is unconsciously used to differentiate the only /ā/ present in the sentence, which we can suppose as being a realisation of [E:], considering that the same grapheme is used for the same phone in the French vowel system. Concerning the examples, these are taken from the corpus subject of this research and are organized as follows: a sample of the Arabizi encoding is given in italics, 85 Pimāla in Arabic means inclination. 86 For further analyses on Pimāla, see Mion (2008b).

followed by a romanisation of the same sentence between slashes, preceding the English translation, which is between quotation marks.

(1) t'hezzni w tgoud feyya lel nessyèn (malla 9assida), /th@zz-ni w tqūd f@-ya l@-n-n@syān (malla qas . īda)/, 'You bring me and lead me through the oblivion (what a poem[!])'.

What is also interesting to note in example 1 is the use of the apostrophe to separate the t from the h, disambiguating the possible match of same with the digraph th, which is used to encode the phoneme /T/. 87 This is indeed a relatively common use of the apostrophe in Tunisian Arabizi, diverging from other Arabizi advanced-systems, where the apostrophe is used to represent the dot of Arabic graphemes, such as Gayn's dot in 3' (Akbar, 2019; Allehaiby, 2013).

Depending on the phonological context, the Pimāla phenomenon can be blocked by the emphatic consonants (C . ) 88 /s . /, /D ˙/, /t . /, /r . / [START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF] and the guttural consonants (G), such as the uvular /q/, /h ˇ/ and /G/, and the pharyngeal /è/ and /Q/ in the following phonological situations: (a) /C . ā/ such as in: /s . āèb-i/, 'my friend', /D ˙āyaQ/, 'lost, confused' or /sbīt . ār/, 'hospital';

(b) /Gā/ such as in: /qāl, 'he said', /h ˇāyb/, 'bad', /Gāli/, 'dear, expensive', /èāža/, 'thing', /Qāyla/, 'family';

(c) /āC . / such as in: /blās . a/, 'square', /èāD ˙r . a/, 'ready' and /šāt . /, 'to kick'.

It can be observed that Arabizi tends towards the use of a faithful phonetic representation of the /a/ phoneme in these contexts, as shown in the following example.

(2) Oussama [...] sahbii l 4ali rak /ūsāma [...] s . āèb-i l-Gāli r . āk/, 'Oussama you are my dear friend'.

87 Unlike many Maghrebi and Mashreqi dialects, in Tunisia, and particularly in Tunis, the dental fricatives are preserved [START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF][START_REF] Mion | Sociofonologia dell'arabo. Dalla ricerca empirica al riconoscimento del parlante[END_REF].

88 Defined pharyngeal consonants are also articulated by retracting the root of the tongue towards the pharynx. In Tunis, the pharyngalisation, or tafh ˇīm, is very light (Durand, 2012). The influence of the emphatic on the adjacent vowel in Arabic terminology is called it . bāq.

With regards to /ī/ and /ū/ vowels followed by pharyngeal phonemes (/è/ and /Q/) at the end of the word, Mion (2008b,a) reports the appearance of a very short [a], which can be explained as a supporting vowel to facilitate the articulation of the phonetics. The scholar traces this vowel manifestation to a phenomenon which is also present in other languages, such as Hebrew and Aramaic, where the phenomenon is traditionally known as pataè furtif. This phenomenon does not seem to be found in the Arabizi data collected during this research, suggesting that this ultra-short vowel has not undergone a phonologising process.

With regards to epenthetic vowels, Durand (2012) asserts that Tunisian follows a Libyan and Near Eastern type pattern, interrupting the /CCC/ sequence to simplify it into /C v CC/, a phenomenon which is also found in Arabizi encoding.

In the following example, it is possible to identify the apical schwa ( @ ) as an epenthetic element, whose function is to interrupt the continuity of the consonantal accumulation between the word boundaries of /kīfā-š/ and the verb that follows it.

(3) Kifech enajmou nt7aslou 3lik ?, /kīfā-š @ n-n@žžm-u n-tèas . l-u Qalī-k?/, 'How can we reach you?'.

Vowel metathesis and diphtongs, in comparison to the Arabic-script system, are easier to note in Arabizi, even if it is not possible to determine the vowel length. Indeed, /ay/ and /aw/ are monophtongised in the intermediate /ē/ and /ō/ in the Hilali dialects and mainly in /ī/ and /ū/ in the pre-Hilali ones. Mion (2008b) reports that in Tunisia, the Hilali confluence, which appeared in the rural speeches of Sāèel, on the eastern coast of Tunisia and in southern Bedouin speech, is maintained by the female speech of Tunis, but has nowadays almost disappeared.

Representation of Arabizi consonants

Tunisian has a conservative consonant system which is expanded by some loan phonemes, such as /p/ and /v/, from the Romance languages, which, in the graphic system in Arabic characters, are represented as and , as for the loanword /brūva/, from the Italian 'prova', meaning 'rehearsal' (i.e. theater rehearsal). The same phonemes in Arabizi tend to be encoded through the same Latin letters, as in the Arabizi word parfanet 'perfumes', which is a result of code-switching with morphological adjustment into Tunisian, as in the case of resturanet, as seen in section 1.3.2. The phoneme /g/, as well as being an allophone of /q/, albeit with rare examples of minimal pairs, which is typical of Bedouin dialects (as opposed to a /q/ realisation typical of urban dialects), is also a loan phoneme as in the following example. 89

(4) 7ell gaz wraja3ou yet9lei, /è@ll l-gaz ˙w-r ˙ažžQ-u y@tqla/, 'Turn on the gas and let it fry'.

In Arabic-script encoding the loan /g/ is usually encoded through the grapheme, i.e.

for /gaz ˙/ of the above example (4), while the /q/ allophone is encoded through , i.e. for /b . agr . a/, a cognate of the MSA word for 'cow'. 90 The glottal stop Hamza /P/ in Tunisian is almost completely absent, with the exception of isolated words and some loanwords from Standard Arabic. 91 In Arabizi, the hamza is usually encoded by the number 2, as in the following example:

(5) 9bal kol chey me jewebni 7ad 3ala sou2eli, /qbal kull š@y ma-žāw@b-ni èadd Qal@ suPāl-i/,

89 It should be specified that there is no such thing as purely /g/ or /q/ speech, but the realisation also depends on the semantic scope or the context of the realisation, as seen in the section 1.1.4.

90 However, this is not to be intended as a regular enconding practice. 91 The hamza vanishing generally produces vowel lengthening of the corresponding vowel or consonant gemination. With regard to the hamza vanishing from the end of the word, this results in compensation lengthening which will also carry the word's accent.

'First of all nobody answered my question'.

As detailed below, the phenomenon of encoding the hamza using the number 2 in Arabizi is not negligible from a quantitative point of view, and in Romanisation, the realisation of the hamza has been reported, even if it is uncertain. In fact, the encoding of this consonant is rather unstable. As expressed above, it can disappear causing a lengthening, or can even be replaced by another consonant, the /h/ [START_REF] Ouerhani | La morphologie verbale du dialecte tunisien: Repères méthodologiques pour un traitement systématique[END_REF][START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF]. Encoding hamza as a '2' in Arabizi could be a form of hyper-correction to a more widespread and 'conventionalised' graphic form of this consonant. Other likely forms of hyper-correction will be expressed in example 15.

The general trend in Arabizi encoding seems to be partly oriented towards phonetic representation and partly driven by the need to make the text comprehensible by reconstructing an orthography that is in some way faithful to the root of the word. This is also true for the consonantal changes that frequently occur in Tunisian words, which are caused by phonemes overlapping and their mutual influence, such in assimilation or emphasis, or emphasis blockage. As an example, [START_REF] Stumme | Tunisische märchen und gedichte: Eine sammlung prosaischer und poetischer stücke im arabischen dialecte der stadt Tunis nebst einleitung und übersetzung[END_REF] states that '/ž/ and /z/ never fit together in the same word'. 92 The same happens when /ž/ meets /z/, or another /ž/ or /s/. This is the reason why, for example, in Arabizi we can find the mixed yoghurt of the well known brand Danone: 'Délice Mamzouj' written as in the following example: 93 (6) delice mamzouz, /delice m@mzūz/, 'delice mixed'.

However, as shown in table 1.3, the main issue in a representation of consonants which is faithful to the phonetics concerns the Arabizi encoding of the emphatic phonemes, such as /D ˙/, /s . /, where for example, the Arabizi out-of-context-word sa7a could encode both the word /s ˙aèèa/, 'health', or the word /sāèa/, 'square'. Regarding /t . /, when Arabizi began to spread, it was generally encoded with 92 'z und z vertragen sieb nie in demselben Worte' [START_REF] Stumme | Tunisische märchen und gedichte: Eine sammlung prosaischer und poetischer stücke im arabischen dialecte der stadt Tunis nebst einleitung und übersetzung[END_REF]. 93 With regard to the /ž/ phoneme, Tunisian, consistently with Maghrebi dialects, presents a fricative [Z] and not affricate [dZ] realisation [START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF].

the number '6' to differentiate it from the voiceless dental plosive /t/.94 As will later be explored in chapter 4, nowadays, only the numbers 5, 3, 7 and 9 have a stable presence in this type of encoding, which respectively encode /h ˇ/, /Q/, /è/ and /q/. Regarding the latter, as explored below, same is rarely replaced by the 'q' grapheme, which is instead associated with the /k/ phoneme. Indeed, Arabizi seems to be more oriented towards a phonetic representation of Tunisian, especially considering the following phenomena:

(a) Consonant gemination (Tašdīd);

In Tunisian, all consonants can be either simple or tense (geminated). Gemination can occur if the consonant in question is preceded by a short vowel and followed by a long or short vowel. In phonetic realisation however, if the tense consonant is preceded by a long vowel, the duplication often disappears, for example, in the active plural participle of the verb , to take: /hāzzīn/ can be pronounced as /hāzīn/. Gemination can be also missed in rapid phonetic realisation [START_REF] Stumme | Tunisische märchen und gedichte: Eine sammlung prosaischer und poetischer stücke im arabischen dialecte der stadt Tunis nebst einleitung und übersetzung[END_REF]. This is particularly true if the geminated phoneme is not followed by a vowel. Such cases are also frequently found in Arabizi representation, as in the following example:

(7) hatha el kol mel 7oub hhhh, /hāDa l-kull m-@l-èubb hhhh/, 'All this because of love ahah'.

(b) Consonant assimilation;

Assimilation is a phenomenon that occurs when a phonological segment modifies the preceding segment or the following segment. This phenomenon may involve the dental phoneme /t/ followed by other dental or sibilant phonemes. The groups [/t/+dentals] or [/t/+sibilants] are definitely not well tolerated in Tunisian, which has been resolved by the assimilation of the /t/, as in , 'don't you remeber?', which has (slowly) been realised as: /ma-tDD@kk@r-š/. If gemination, resulting from an assimilation, is found at the beginning of the word, it is not heard in the phonetic realisation unless the /t/ is supported by the insertion of an epenthetic vowel.

This is also the case in example 8, where the passive morpheme /t-/ should not be heard due to it being at the beginning of the verb /Dk@r/, 'to mention'; however, it is represented in Arabizi. There are also other types of not-represented assimilations in final or intermediate positions of the word, as in the case of the Arabizi word 'jedti', which is realised as ["Z@t:i], 'my grandmother', in the first word of example 9.

(8) ethika leblassa elli t'thakret fi souret el ka7f, /hāDīka l-blās ˙a @lli t-D@kr@t fī sūr@t-@l-kahf/, 'This is the place mentioned in the Surat Al-Kahf'.

(9) jadti mel om tbajal wled wledha 3lina, /ž@dt-i m-@l-ūmm tb@žž@l wlād wlād-ha Qalī-na/, 'My maternal grandmother prefers her children's children to us'.

(c) Consonant assimilation: the definite article.

With regard to article assimilation, this normally occurs when followed by a coronal consonant, which are consonants articulated with the tongue blade raised towards the dental, alveolar, or palato-alveolar region. 95 In Arabizi, this is easily identifiable (Gugliotta et al., forthcoming), as in the following example:

(10) Inchalah cycle ejjay wala eli ba3dou, /nšāl ˙l ˙a cycle @ž-žāy walla @lli baQd-u/, 'God willing next time, or the time after that'.

As can be seen in example 10, in Tunisian, as in other Maghrebi dialects, there are new consonants which are subject to the tafh ˇīm phenomenon, whose pharyngalised (or emphatic) realisation results in /l . /, /m . /, /r . /, /b . /, /z . /. Coming back to the topic of article assimilation, this may not appears in Arabizi encoding, mostly in the cases where the article and the defined name (starting with a coronal phoneme) do not form a single graphical compound and there is white space between them. These cases generally occur when the article is graphically joined to the preposition, as in the following examples:

(11) wa9t yji nghar9ou bil dlel, /waqt yži nGarq-u b-@d-dlāl/, 'When he comes I drown him with vices'.

(12) entouma yelzemkom derss fil jografia, /ntūma y@lz@mkum d@rs fī-ž-žuGr . āfya/, 'You(.pl) need a geography lesson'. This mode of writing hinders the representation of assimilation, because the article, when separated from the noun it defines and linked to the preposition that precedes it, is always represented as 'l'. In addition, Tunisian Arabizi seems to prefer the formation of a compound between prepositions and articles separated from the noun, rather than a compound formed by the three (see chapter 4). Conversely, the article tends to appears in its full form as a unique token when the determined name does not begin with the coronal phonemes that generate assimilation. Two examples are given below, the first one (13) shows a preposition, the article and a noun not beginning with a coronal phoneme, and all of these elements are graphically isolated. While, the second one (14) shows a similar case, this time not involving a prepositional sentence, but only a nominal phrase, where the noun starts with a loan phoneme (/p/), being the noun a hybridism from the English word 'penalty'.

(13) nebki min il far7a, /n@bki m@n @l farèa/, 'I cry for happiness'. ( 14) ki zayech dhaya3 el pilanti, /ki zayyāš D ˙ayyaQ @l-pīlānti/, 'When Ziyech missed the penalty'. This is a hyper-correction practice, which can be connected to the Tunisian ideological question of the perception of the writing system, with regards to the Arabic-script-encoding of 'CCC' clusters or words starting with a '#CC' pattern, which is not allowed in Standard Arabic. Since the Arabic script is the encoding system used for the acrolect, using it seems to activate in the writer a form of respect for the prestigious Arabic's norm. Such hypercorrective tendencies bring us back to the earlier discussions on diglossia (section 1.1.3), and in particular to the perception, in this case, of one orthographic system as being more prestigious than another as a system which is habitually dedicated to the encoding of the prestigious variety. Perhaps the same cognitive process that led to the writing of medieval texts in that form of Arabic mixed with elements of Standard Arabic and Neo-Arabic and pseudo-correct features, which [START_REF] Blau | The state of research in the field of the linguistic study of middle Arabic[END_REF] defines as Middle Arabic, in this context, leads the author of the written text to produce an encoding which is not so much of transcriptive type, but is more transliterative, having the orthography of Arabic characters as a reference source system. The hyper-correction, in this case, consists in representing the cluster below with opening epenthetic vowels (codified through the Palif grapheme), in order to transform it in a different cluster (#VCC) which is easier to realise. This phenomenon is much less commonly found in Arabizi, but is still present, as in the following example, where an epenthethic vowel can be seen at the beginning of the word tkūn: 96 96 This phenomenon has already been encountered in example 14 in the Arabizi word entouma, which can be traced back to the Tunisian-Arabic-script form .

(15) t'es dans une ces regions donc yelzmek etkoun men mdina men ces regions alors que t'es mehdwi, /t'es dans une ces regions donc y@lzm@k tkūn m@n mdīna m@n ces regions alors que t'es m@hdwi/, 'You are in one of these regions so you should come from a city of these regions, that should make you a Mehdia citizen'.

Arabizi code-switching

This chapter has documented how the variant of the Tunisian dialect, which has emerged as the national variety, coincides with that of the capital. It has also documented how this urban variety represents, at the diastratic level, a high social stratum with access to a French cultural and linguistic background, being rich in French code-switching (CS) elements. The definition of code-switching adopted here is that of [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF], who states that the term is used to refer to alternations of linguistic varieties within the same sentence (intrasentential).

'CS is the selection by bilinguals/multilinguals of forms from two or more linguistic varieties in the same conversation. [...] Stretches of CS material may be inter-sentential (switches from one language to the other between sentences) or intrasentential (within the same sentence, from the single morpheme level to higher levels).'

Considering the two kinds of asymmetry involved in code-switching, i.e. the structural and the content asymmetry, [START_REF] Myers-Scotton | Carol myers-scotton: Multiple voices: An introduction to bilingualism[END_REF] explains that, in a bilingual context, one language supplies the main grammatical framework for a clause containing words from both languages. The scholar defines this structuraldriver-language as the Matrix Language (ML), while the content-driven-language is the Embedded Language (EL). As mentioned earlier, from the perspective of [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF] this can be seen as in the previous example of Tunisian re-morphologisation of the French word 'restaurants', r@stūrānāt, where the ML coincides with Tunisian Neo-Arabic, while the EL coincides with the French language. 97 The ML not only governs the form of a word selected from the EL lexicon, but also governs the structural relationships between words within the sentence. A code-switching model for bilingual speech is the Matrix Language Frame Model (MLF), while a model specialised in the morpheme types is the 4-M model [START_REF] Myers-Scotton | Carol myers-scotton: Multiple voices: An introduction to bilingualism[END_REF]. At the same time, other scholars, such as 97 We considered this to be Tunisian Neo-Arabic and not Standard Arabic for two main reasons: the first one being the specific lexicon, as Standard Arabic has its own widely-used term to express the concept of restaurants; the second reason is the Pimāla applied to the female-ending morpheme, which, even if it also applied to Standard Arabic, is mainly a characteristic of non-Standard Arabic phonetic realisation. [START_REF] Berruto | Fondamenti di sociolinguistica[END_REF]; [START_REF] Regis | Appunti grammaticali sull'enunciazione mistilingue[END_REF]; [START_REF] Poplack | Introduction: How languages fit together in codemixing[END_REF], consider these hybrid forms of words as phenomena more analogous to lexical borrowing. With regards to MLF, it makes a distinction between content morphemes and system morphemess, where the first are those which assign or receive thematic roles, 98 while the second of the two are basically functional words, even if the two categories do not overlap completely. The 4-M model refines the MLF model by dividing system morphemes into three types:

⋆ Early system morphemes, morphemes which are conceptually activated by a speaker's pre-linguistic intentions. As an example, the Tunisian determiner for the French word 'famille' was chosen in the following sentence, and this phenomenon is very common [START_REF] Tarquini | musica rap e convergenza linguistica in arabo tunisino[END_REF]. ( 16) hatta el famille walit j evite tout le monde, /èatta l-famille wallīt j'evite tout le monde/, 'Also the family I started to avoid everybody'.

The other two systems are defined as being 'late', that is to say, they are not activated until a first production level. ⋆ Bridge late system morphemes, which are activated to fully form the relationship between elements of a syntagm. As an example, the following sentence, as well as showing the Tunisian determiner for the French proper name Cap Bon, 99 shows the pseudo-preposition /mtāQ/, which is used for the possessive analytical construction, followed by a French indefinite nominal syntagm une zone. It should be noted that the indefinite French article is used, instead of the Tunisian one, which is ø. 100 (17) S7i7, ama essa7el fi tounes hiya tasmya mta3 une zone, kima el cap bon..., /s . èīè, āma @s-sāè@l fi tūn@s h@ya t@smya mtāQ une zone, kīma @l-cap bon.../, 'That's right, but the Sahel in Tunisia, is a nomenclature of a zone, as the Cap Bon'.

98 Also called T-roles, which are mainly carriers of semantic cores. 99 Cap Bon in Arabic, , or Rās @d-Dār, which was known in antiquity as the Cape of Mercury, or Promontorium Mercurii, is a cape and peninsula at the northeastern tip of Tunisia, i.e. the northwestern end of the Gulf of Tunis [START_REF] Shaw | Or Observations Relating to Several Parts of Barbary and the Levant: Illustrated with Cuts[END_REF]. 100 As seen in 1.2 table, the use of the analytical construction of genitive phrase is an innovative pre-Hilali trait. Further phenomena of contact with the French language at the orthographic level can be observed in terms of the '-e' which has been added at the end of french loanwords, as in the word /Qars/ in the following example (Durand, 2012). ( 19) pour tous vos èvènement lamet enfaset 5outba 3arsse anniversaire je vous propose des petits fours, /pour tous vos événements lammāt nfāsāt h ˇut . ba Qars anniversaire je vous propose des petits fours/, 'For all your events: meetings, puerperia, reunions, engagements, weddings, birthdays, I propose you small pastries'.

Arabizi extralinguistic features

Typical characteristics of a Digital Networked Writing (DNW), according to [START_REF] Thurlow | The language of text-messaging[END_REF], are Typographical-cum-linguistic devices, such as onomatopoeia (i.e. hahah) or exaggerated use of spelling and punctuation (i.e. no moooore!!!! ), pragmatic use of capitalization, such as STOP IT. [START_REF] Heath | Orthography in social media: Pragmatic and prosodic interpretations of caps lock[END_REF], spacing, and special symbols for adding the missing prosody and emphasis impact which is obviously missing in an DNW text. All of these are also found in Arabizi. The only devices less represented in Tunisian Arabizi DNW are abbreviations, which are usually consonant clusters (i.e. thx, meaning thanks), or clusters of numbers and letters defined by Androutsopoulos (2011) as rebus-like spellings (i.e. 2l8 having the meaning too late), and acronyms, which are Chapter 2 A Tunisian Digital Networked Writing Corpus

Introduction

The development and diffusion of the Internet, together with that of technology, has transformed written communication, leading to the emergence of a new specific mode: electronic written communication. This new form of communication, which has spread rapidly since the mid-1990s, has also raised many concerns. Technology in general has had a great impact on our daily habits, including the way we read the news, the job market and careers, the way we socialise and the way we express our identity.

Technology has also had a major impact on language use and the way we relate to each other. In fact, research which deals with this phenomenon ranges from discourse analysis with approaches from the communication sciences to psychology and sociology, and the methodological way in which same is analysed is comparative with that of other discourse modalities or that focused on specific linguistic phenomena. This second chapter addresses this particular topic, which has carved out its own space within linguistic studies over the last three decades. Section 2.2 will offer a general definition of Digital Networked Writing, while the following sections are intended to describe the methodological choices made with the aim of building a representative and balanced corpus of Tunisian Arabizi, in both the fields of Corpus Linguistics (section 2.3) and Deep Learning (section 2.4). Regarding these fields, the main strategies and principles that govern the art of building linguistic corpora and the art of building artificial intelligence tools with Deep Learning techniques in order to allow machines to handle linguistic problems will be discussed. Finally, the last section (section 2.5) presents the state of studies dedicated to Arabic Natural Language Processing (ANLP), in particular regarding Tunisian in both scripts, i.e. Arabizi and Arabic characters.

Defining Digital Networked Writing

Electronic communication refers to a form of communicative exchange where messages are conveyed by electronic systems, i.e. based on the combination of informatics and telecommunications, from mobile phones or computers, through the Internet. Electronic communication is therefore a generic term that encompasses many different types of communication situations, whether oral or written, private, public or semipublic, messaging or computer-mediated, synchronous or asynchronous, etc. Regarding written communication which is mediated by computer, this is often defined as Computer-Mediated Communication (CMC). Nowadays, given the spread of smartphones, Communication Mediated by Telephone (CMT) is also included within CMC, despite previously consisting of a distinct category. In fact, the distinction is no longer very significant today in light of the arrival of web 2.0 (in the late 90s), and electronic communication can instead be subdivided according to the different channels and scopes of communication, and, in the case of these research aims, by writing. The mode of communication of blogs, compared with applications designed for discussion, such as forums or those for instant messaging, which may also include Facebook, certainly has its peculiarities. A blog is a kind of diary which is shared on the Internet, in which texts, of no predetermined length, are ordered chronologically and can be enriched by multimedia elements, such as photos, videos, and audio. It is possible to interact with them, but it is an asynchronous form of communication. Forums are instead generally organised by topics and require registration. Users are not required to provide their true identity and may use pseudonyms. However, it is not unusual to share a sense of belonging and friendship with forum users, who often find themselves connected simultaneously or discussing shared issues. This possibility has lately been denied to Facebook users, who for security reasons are now being asked to prove their real identity [START_REF] Ziamari | Matériaux d'enquêtes autour des' usages jeunes' dans quatre villes marocaines casablanca, meknès, tétouan, marrakech[END_REF]. Communication on the Facebook platform mainly falls under one of two types, asynchronous and synchronous, and is carried out either through long posts, or through comments on others posts, which can be public or private, and addressed to users who are part of your circle of friends, to a specific group or to anyone who is part of the Facebook community (in the case of completely public profiles). With regards to the name Digital Networked Writing, this refers to writing practices in digital environments. [START_REF] Schmitz | auswirkungen elektronischer medien und neuer kommunikationstechniken auf das sprachverhalten von individuum und gesellschaft[END_REF] Here, non-linear communication refers to hypertextuality, the networking function of new media that allows a large quantity of information to freely move around within a series of interconnected nodes in the network. A number of terms has been created to highlight the specificity of different electronic communication practices, such as electronic communication by [START_REF] Murray | When the medium determines turns: Turn-taking in computer conversation. Working with language: A multidisciplinary consideration of language use in work contexts[END_REF], who examines turn-allocation techniques in non-linear organisations of CMC discourse. [START_REF] Panckhurst | Le discours électronique médié: bilan et perspectives[END_REF] uses two French expressions, based on the neologism médier : communication médiée par ordinateur and discours électronique médié,1 explaining that they were adopted from reflections on the role of the instrument during the communication process, referring to the studies of Vygotsky [START_REF] Vygotsky | Pensée et langage[END_REF][START_REF] Bronckart | Vygotsky aujourd'hui. Delachaux et Niestlé[END_REF]. She indeed justifies the neologism by explaining that the verb médier is more appropriate than the verb médiatiser because electronic communication is truly mediated (modified) in the Vygotskyian sense of being influenced by the extra-linguistic context, and not merely being broadcast through a medium. [START_REF] Baron | Always on: Language in an online and mobile world[END_REF] and [START_REF] Anis | Communication électronique scripturale et formes langagières[END_REF] also chose similar solutions, respectively: electronically-mediated communication and communication électronique scripturale. In order to distinguish between the different characteristics of monologic and dialogic communication, scholars have also adopted specific terminology for the latter, such as computer conversation by [START_REF] Murray | When the medium determines turns: Turn-taking in computer conversation. Working with language: A multidisciplinary consideration of language use in work contexts[END_REF], or écrit interactive et dialogique by [START_REF] Anis | Texte et ordinateur: l'écriture réinventée[END_REF]. However, the more popular name for this type of communication in a very broad sense remains Computer-Mediated Communication (CMC), which focuses on the medium itself [START_REF] Herring | Computer-mediated communication: Linguistic, social, and crosscultural perspectives[END_REF]. Netspeak is the solution proposed by [START_REF] Crystal | Language and the Internet[END_REF], as an alternative to Netlish, which stresses the concept of an everyday more multilingual Net, or to the terms Internet language and cyberspeak, which focus more on the technological side of CMC. He underlines the fact that: 'As a name, Netspeak is succinct, and functional enough, as long as we remember that 'speak' here involves writing as well as talking, and that any 'speak' suffix also has a receptive element, including 'listening and reading'.'

With regards to classification, [START_REF] Anis | Communication électronique scripturale et formes langagières[END_REF] proposed a division of electronic communication types based on instrument properties, such as the available space for the text, the temporal processing of communication (synchronous, asynchronous), the type of readers to whom the message is addressed (individual, group) and indeed the type of electronic support (computer, phones). Through these parameters, [START_REF] Anis | Communication électronique scripturale et formes langagières[END_REF] identifies six types of electronic communications, namely: e-mails, mailing lists, forums, instant messaging, chats and SMS (Short-Message Service). With regards to the type of reader, some platforms, such as Facebook, offer multiple modalities, as mentioned above. The offered modalities include the possibility of having a private communication with a specific person, or sharing content with a group of people. The first modality is usually synchronous, while the second one it is not necessarily synchronous, instead resembling the forums or even the blog writing genre. [START_REF] Marcoccia | Analyser la communication numérique écrite[END_REF] observed that the asynchronous texts (e-mails, mailing lists, blog, forums) are usually the most formal ones, making use of a writing style which is much closer to the standard style, for reasons that he traces back to the fact that texts of this type are more permanent because they can be archived. On the other hand, synchronous conversations make use of writing styles closer to the oral communication style, due to same not being persistent. [START_REF] Herring | A faceted classification scheme for computer-mediated discourse[END_REF] classifies computer-mediated discourse in a scheme based on features, which she refers to as facets, applying the [START_REF] Hymes | Foundations in sociolinguistics: An ethnographic approach[END_REF] The S1 parameter refers to the intended participation modality, which can be oneto-one, one-to-many, many-to-many or a closed group, public or private, anonymous or using nicknames. The participant characteristics parameter describes participants' profiles, which documents them as belonging to specific cultures, having their reallife status or role, their skills in CMC, as well as ethnographic factors, such as age, gender, etc. 3 The S3 parameter refers to the group's interaction aims, while S4 is about the specific topics, if there are any. The tone parameter focus on the formality or standard attitude within the group, while S6 concerns the main activities of group users. Finally, the norms parameter refers to the group rules, concerning organisation, social behaviour, and languages, while S8 focuses on the latter, namely the language variety, the font and the writing system. This last parameter is particularly relevant to our research, which concerns the construction of a Tunisian Arabizi corpus, where, as we saw in chapter 1, Arabizi is an encoding system which was created specifically for electronic writing. In general, the rapid and consistent diffusion of electronic writings made available to social sciences and language scholars has created a large repository of data, which is easily accessible, albeit with certain limitations. In general, there is a positive aspect to studying linguistic phenomena through a corpus, due to the zoom lens effect that allows the observer to easily detect the phenomena that he is looking for [START_REF] Duchet | Corpus massifs et corpus bilingues alignés: leur impact sur la recherche linguistique[END_REF]. At the same time, it has been proven that, in order to observe certain specific structures of the language, such as collocations, a quantitative overview is needed [START_REF] Kraif | Collocations et linguistique de corpus: l'intuition des linguistes et les critères quantitatifs convergent-ils?[END_REF]. However, the positive aspect of using DNW data for linguists' work is the fact that DNW data collection allows the researcher to avoid the observer's paradox as described by [START_REF] Labov | Sociolinguistic Patterns[END_REF]:

'[T]he aim of linguistic research in the community must be to find out how people talk when they are not being systematically observed; yet we can only obtain these data by systematic observation.' This is not the only problem to exist in DNW data collection. There are also the ethical considerations connected to what is considered to be public or private, and how to protect sensitive user data when collecting his texts. These topics will be further analysed within the following section, in particular in section 2.3.4.

On the basis of Herring's studies it is therefore possible to state that the DNW does not constitute a single textual genre, but is rather a collection of textual microgenres brought together by the common characteristic of being forms of writing which are supported and mediated by the use of electronic devices. In fact, quantitative approaches, such as that of [START_REF] Panckhurst | Le discours électronique médié: bilan et perspectives[END_REF], 4 may reveal divergent trends among different DNWs, i.e. chat, forums, and email. As an example, [START_REF] Anis | La dynamique discursive d'une liste de diffusion: analyse d'une interaction sur «typographie@ irisa. fr». Les Carnets[END_REF] shows the formality traits of a typical mailing list register. As previously mentioned, a productive discussion about DNW is the assignment of its subgroups to the written vs. oral or hybrid class. A number of scholars have been working on DNW feature classification of the written or oral type, with the most rational conclusion that can be drawn being that DNW represents the hybrid nature of a graphic-speech that contains both written-like and speech-like characteristics [START_REF] Gadet | Ubi scripta et volant et manent[END_REF][START_REF] Anis | Texte et ordinateur: l'écriture réinventée[END_REF][START_REF] Georgakopoulou | Computer-mediated communication[END_REF]. As early as the 1980s, the view of a continuum of spoken and written language manifestations had started gaining ground, a proposal which was advanced by [START_REF] Tannen | Spoken and written language: Exploring orality and literacy[END_REF] from the observation that, for example, speech can follow a script and writing can be colloquial. In fact, since the early days of CMC research, the blending of the written and oral types in employing oral-like strategies for electronic writing has been evident [START_REF] Gadet | Une distinction bien fragile: écrit/oral[END_REF], as reported also by [START_REF] Hert | Quasi-oralité de l'écriture électronique et sentiment de communauté dans les débats scientifiques en ligne[END_REF] There is no doubt that electronic writing is primarily a form of writing. Panckhurst ( 2006) reports a number of features of written discourse, such as interrogative forms, circumfixed French negation etc., which are also present in DNW texts, particularly with respect to such phenomena in the typology of asynchronous DNW. On the other hand, as Hert points out, electronic writing lies along a continuum between written and oral, where it can draw on the characteristics of both forms. For example, oral discourse is generally improvised and spontaneous, which lumps it in with chatroom conversations. It has already been stated that some types of DNW are more formal than others, namely the asynchronous ones, such as e-mails, mailing lists, blogs, and forums, in comparison with the synchronous ones, such as text messaging and instantaneous chatting services. Imagining the extremes of the above continuum as constituted by the written and oral poles, it is therefore possible to assume that it is the register (formal/informal) that motivates the use of traits which are more inclined to 5 'The form of the electronic debate certainly induces a continuity in style with other forms of writing (summary notes, reading notes, conference texts, etc.). However, this mode of writing brings into existence forms of interaction that are close to the properties of oral exchanges. Through the practices of repetition, quotations, and synthesis, through the use of the context of the exchanges, through the temporal unfolding of the debate in parallel with other events, this quasi-orality of writing is constructed [...] It consists, in this work, on writing with the aim to introduce properties of oral exchange, such as collective participation in what is being said, the definition in situation of the context of the exchange, a feeling of community [...].' My own translation.

the written-organised-formal-pole or the oral-spontaneous-informal pole [START_REF] Panckhurst | Le discours électronique médié: bilan et perspectives[END_REF]. [START_REF] Marcoccia | Analyser la communication numérique écrite[END_REF] in fact supports the same thesis and also includes the binomial monological/dialogical in the subdivision between the two poles. Section 1. 3.3 has already explored some typical characteristics of DNW, which [START_REF] Thurlow | The language of text-messaging[END_REF] defines as typographical-cum-linguistic devices, such as onomatopoeia or exaggerated use of spelling and punctuation (with more of an expressive than a syntactic function [START_REF] Anis | Pour une graphématique des usages: le cas de la ponctuation dans le dialogue télématique[END_REF]), pragmatic use of capitalisation, spacing, and special symbols for adding the missing prosody and emphasis impact which are obviously missing in a DNW text. According to [START_REF] Gadet | Ubi scripta et volant et manent[END_REF], these devices can be traced back to the logic of fictitious and performed orality, that we often encounter in the DNW of chats (and which is almost absent in blogging, for example), as brands of familiarisation with other participants through a kind of mimicry of the prosody and emphasis of pretended-oral-discourse. According to [START_REF] Crystal | Language and the Internet[END_REF], these are mainly devices which are used in order to fill the pragmatic gap due to being physically non-present during the conversation, which leads the user to respect the four conversational maxims of the philosopher H. P. Grice for an efficient co-operative use of language, namely: The Maxims of Quality, The Maxim of Relevance, The Maxim of Quantity and The Maxim of Manner. [START_REF] Marcoccia | Analyser la communication numérique écrite[END_REF] distinguishes five levels of DNW characteristics:

1 -Typographical: Integration of non-alphabetic symbols in text (such as @), non-standard capitalization, emoticons.

2 -Orthographic: Use of phonetic spelling (phonetic-based substitutions, i.e. 'koi' for French 'quoi').

3 -Morphological: Use of abbreviations, word truncation, use of acronyms, etc.

-Lexical:

Use of specific vocabulary associated with youth and CMC vocabulary.

-Syntactical:

Use of telegraphic or fragmented syntax, with the loss of some sentence components, such as articles.

The frequent use of 1 st and 2 nd person pronouns vs. the 3 rd , as well as the extensive use of modal verbs [START_REF] Yates | Oral and written linguistic aspects of computer conferencing[END_REF].

Corpus Linguistic Standards for DNW Corpora

'A corpus is a collection of pieces of language text in electronic form, selected according to external criteria to represent, as far as possible, a language or language variety as a source of data for linguistic research' (Sinclair, 2004, 19). This is the definition of a corpus given by [START_REF] Sinclair | Developing linguistic corpora: A guide to good practice corpus and text-basic principles[END_REF]. Before describing the building of the Tunisian Arabish Corpus (TArC), it is necessary to give a general overview of Corpus Linguistic (CL) standards, starting from the external criteria mentioned in the above definition of a corpus.

General Principles and Criteria in Data Collection

The guiding principles for corpora building are criteria that are not strictly definable, but are based on the common sense and good thinking of those involved in the corpus building. Feedback from users is also of great value in order to improve the structure of the corpus. The concepts generally associated with a good corpus structure are those of representativeness and balance. According to [START_REF] Sinclair | Developing linguistic corpora: A guide to good practice corpus and text-basic principles[END_REF], in order to achieve the most representative corpus possible, structural criteria, which are few in number and easy to respect, should be decided a priori. Following the structural criteria, the corpus builder (CB) should identify the text genres that meet them in order. With regards to the aim of balance, the CB should avoid texts which are too specialized, unless those coincide with the specific target texts of the corpus. Other common structural criteria are documented as follows:

1. Text's mode: speech, writing, DNW, etc.;

2. Text's genre: romance, journal, blog, etc.;

3. Text's domain: academic, street language, etc.;

4. Text's language: language or variety;

5. Text's location: the place of origin;

6. Text's date: date or period. According to [START_REF] Sinclair | Developing linguistic corpora: A guide to good practice corpus and text-basic principles[END_REF], the following table outlines a general strategy for corpus building:

1.

Collect texts according to their communicative function and not their linguistic contents.

2.

Commit toward the highest possible degree of representativeness.

3

Avoid basing contrastive analyses on contrastive structural components of the corpus.

4.

Choose criteria a priori; these should be few, well separated from each other, and efficient as a set of reference points.

5.

Separately store information about the texts.

6.

Collect samples of the language which are as complete as possible.

7.

Document corpus design steps.

8.

Be guided by representativeness and balance, even if these are not precisely definable concepts.

9.

Exert control on the topic of texts driven only by external criteria, not internal ones.

10. Aim for homogeneity, avoiding unusual texts. With regards to the first and second points, these concern any kind of corpus building, while the third point mainly concerns inbuilt contrastive corpora, such as the parallel ones (involving more than one language), which is not the case of the Tunisian Arabish Corpus (TArC). This can also be considered in the case of a diachronically contrastive corpus. According to the fourth point, criteria for TArC building have been chosen a priori and are outlined in table 2.6. With regards to the fifth point, this concerns information on the structure of texts and explains that texts should be separated from plain text. The information can be about the provenience of the text or about the language contained in it, in that case, usually: annotations. The annotation structure of TArC will be described in Chapter 3. Concerning the ninth point, no control was exerted over the topic of the text. However, in order to detect DNW Tunisian websites and in order to build a corpus which is as representative as possible of the linguistic system, it would be useful to identify wide thematic categories that could represent the most common topics of daily conversations on DNW. In this regard, two instruments with a similar thematic organisation have been employed:

1. 'A Frequency Dictionary of Arabic' and in particular its 'Thematic Vocabulary

List' (TVL) [START_REF] Buckwalter | A frequency dictionary of Arabic: Core vocabulary for learners[END_REF];

2. The 'Loanword Typology Meaning List', which is a list of 1460 meanings (LTML) [START_REF] Haspelmath | The loanword typology meaning list: Electronic databases of 29 languages. A collaborative project coordinated by the Max Planck Institute for Evolutionary Anthropology[END_REF].

The TVL consists of 30 groups of frequent words, each one represented by a thematic word. The 'Loanword Typology Meaning List' is the result of a joint project by Uri

Tadmor and Martin Haspelmath: the 'Loanword Typology Project' (LWT), which was launched in 2004 and ended in 2008. The LTML consists of 23 groups of basic meanings sorted by a representative heading word. Considering that the boundaries between some categories are very blurred, some categories have been merged, such as 'Body'

and 'Health'. Some others were not taken into consideration due to their irrelevance to the purposes of our research, e.g. 'Colors', 'Opposites', 'Male names'. In the end, the focus of this work will be 15 macro-categories. With the aim of easily detecting texts and their respective URLs, without introducing relevant query biases, it was decided that the category names should not be used as query keywords [START_REF] Schäfer | Web corpus construction[END_REF]. Therefore, we associated a set of Tunisian Arabizi keywords belonging to basic Tunisian vocabulary to each category. It was found that three meanings belonging to the semantic category were enough to obtain a sufficient number of keywords and URLs for each category. For example, for the category 'Family', the meanings 'son', 'wedding', 'divorce' were associated with all their Arabizi variants, obtaining a set of 11 keywords (Gugliotta and Dinarelli, 2020b,a).

1. Text's mode: DNW informal written texts.

2. Text's genre: blogs, forums, social network posts and related comments.

Rap lyrics.

Text's domain:

Computer Mediated Communication (in particular DNW).

4. Text's language: Tunisian (encoded in Arabizi).

5. Text's location: users' origins.

6. Text's date: publication date. As shown in table 2.6, which details the criteria adopted to build TArC, in addition to the first three textual genres, which are related to DNW context, the decision was made to also collect text of rap lyrics, which were spontaneously written on dedicated forums by their users. This choice was motivated by the hypothesis that these texts could represent a useful tool for orthographic comparison (between the two systems of Arabizi and Arabic-characters script), since these texts use a mixture of both systems.

The slightly different nature from the target of the other textual genres in TArC is not considered to invalidate the consistency of the contents of TArC, considering that TArC is structured in such a way as to be flexible in isolating each textual genre, so that, in the case where users of TArC want to carry out analyses on the texts of TArC, except for those of rap, they can easily extract the latter, working only on the other three genres. With regards to the location criterion, this coincides with metadata, which is collected only if made available by the users themselves. We decided not to record the exact hometown of users, instead recording the governorate to which it belongs, in order to comply with the users' right to privacy, a topic that will be discussed in section 2.3.4. The 24 governorates of Tunisia (wilāyāt) represent the first level of territorial subdivisions of the country, and have been reported in TArC under their French names.6

Context and Metadata

The DNW data collection presents a methodological problem which requires attention.

This concerns the link between observed phenomena and the contexts, in other words, the possibility of being phenomena context-dependent variables. [START_REF] Sinclair | Developing linguistic corpora: A guide to good practice corpus and text-basic principles[END_REF] explains that ideally a corpus should be designed by a researcher who is knowledgeable about the language context and the speech community whose language is to be represented by the corpus. He further explains that semantic content should be of secondary importance during text selection, and that texts should be selected regardless of content. It will be difficult for the observer, due to the lack of the context, to understand whether the observed phenomenon is context-dependent or not. According to Marcoccia ( 2016), there are two strategies for addressing this problem, a text-centered solution and context reconstruction. The first of these takes into account the fact that the context cannot be reconstructed, such that the observer acts as if it doesn't exist, dealing with the texts as de-contextualised texts by default. The second of these instead tries to reconstruct the contexts considering that the same texts provide all the necessary information to contextualise it. These two positions are quite radical, considering that in some occasions it is really easy to extract the context from a conversation, and in this case, dealing with the text as de-contextualised by default may mean that the observer loses part of the information. On some other occasions, it is not really possible to re-construct the context, and for that reason, a strong effort in this direction could cause the observer to misrepresent it. With regard to the understanding of whether a phenomenon is context-dependent or not, a good number of diversified texts can ensure that the observer has the right point of view.

Concerning the corpus building stage, a good practice to use when recording contextual information is the recording of metadata, namely data about data [START_REF] Burnard | Metadata for corpus work[END_REF]. Linguistic corpora should be designed to support different types of analyses and this is the reason for providing a metadata framework which is as complete as possible.

CL acknowledges different strategies for providing texts with information, such as the Extensible Markup Language (XML) and in particular, the Text Encoding Initiative (TEI), the Open Language Archive Community (OLAC), and the ISLE Metadata Initiative (IMDI) [START_REF] Burnard | Metadata for corpus work[END_REF]. Depending on the aims behind corpus collection, it is not strictly necessary to encode information through this type of formalism, which is commonly criticised for being too wide and complex [START_REF] Lancioni | Encodings, genres, texts: Issues in Arabic corpus linguistics[END_REF]. In fact, computer systems are now able to extract information from texts organised in comma or tab separated values or tabular format (CSV, TSV or TAB). One strong point of these formats is that corpora encoded in CSV or TAB can easily be converted into other formats, such as the Excel format (XLSX), which makes these formats readable through other software or tools, such as Sketch Engine.7 At the same time, in the case of corpora designed with a specific use in terms of linguistic analysis, the type of metadata that must be recorded can be very complex (e.g. annotations of turns of phrase and general discourse sub-units). Not only is it difficult to automatically extract this type of metadata, requiring manual work, but it also requires a format that allows for deep levels of annotation, such as XML or TEI formats, unlike other formats that are more operational for NLP (CSV or TAB), but which are structurally poorer. In general, it is better to avoid building a corpus in commercial word-processing programs, such as Microsoft Word, because it cannot be processed by a corpus analysis tool [START_REF] Wynne | Archiving, distribution and preservation[END_REF]. In addition, if the corpus is stored in CSV or TAB, it will be available regardless of whether a specific software is valid or if it has been updated. TArC is stored on GitHub, a Git repository hosting service.8 While Git is a command line tool, GitHub provides a Web-based graphical interface. It also provides access control and several collaboration features. TArC files are made available, sorted by genre in both TAB and XLSX, and are continuously updated. However, every update, even small ones, is recorded by the system, so it is possible to go back to previous versions. Concerning the metadata collected in TArC, as shown in table 2.7, this is metadata about the texts and metadata about the users.

Text metadata User metadata.

Genre (Forum, Blog, Social Network, Rap). Gender: Male (M) and Female (F). Governorate of belonging.

Data annotation

'Corpus-builders do not in general have the leisure to read and manually tag the majority of their materials; detailed distinctions must therefore be made either automatically or not at all [...] In the simplest case, a corpus builder may be able reliably to encode only the visually salient features of a written text such as its use of italic font or emphasis, or by applying probabilistic rules derived from other surface features such as capitalisation or white space usage.' [START_REF] Burnard | Metadata for corpus work[END_REF].

Annotating a corpus refers to adding interpretative, linguistic information to the corpus texts, such as marking a word in a text as corresponding to a particular Partof-Speech (POS tagging), based on both its definition and its context, or reducing an inflected form of a word to its lemma (lemmatisation) [START_REF] Leech | Corpus annotation: Linguistic information from computer text corpora. chapter Introducing corpus annotation[END_REF]. Depending on the purpose of the corpus, annotations can be of different types, and in some cases, CBs prefer not to annotate the corpus at all in order to investigate it in its pure form, or to avoid same being affected during analysis by any annotation errors or problems due to not highlighting phenomena compared to others which are marked by a tag.

Regarding the latter, this is the case described by [START_REF] Sinclair | Developing linguistic corpora: A guide to good practice corpus and text-basic principles[END_REF] concerning the fact that if the data in the corpus can only be observed through the tags, anything that the tags are not sensitive to will not be noticed. In other cases, annotation is seen as enriching the text and supporting its use [START_REF] Leech | Chapter 2. adding linguistic annotation[END_REF][START_REF] Leech | Corpus annotation: Linguistic information from computer text corpora. chapter Introducing corpus annotation[END_REF]. With respect to TArC, levels of annotation of texts and words are provided, due to the fact that in an under-researched language such as Tunisian, which is encoded in a non-standardised orthographic system, text annotation could be considered to be of added value to the corpus. There are two main reasons for this: to support linguistic analyses on TArC and to support the development of tools for automatic processing or as a benchmark for developing web-based interfaces for Tunisian learners (Gugliotta et al., 2020;[START_REF] Granger | Integrating learner corpora and natural language processing: A crucial step towards reconciling technological sophistication and pedagogical effectiveness[END_REF]. As will be discussed in section 2.4, with regard to automatic processing, the tools that Natural Language Processing have made available today allow us to expand the possibilities of an annotator, who, as we will see, contrary to what Burnard says in the quotation above, does not have to manually annotate an enormous amount of data, but must instead post-edit the product of an automatic processing of data, thus reducing the amount of errors in the corpus. On the other hand, although the support of NLP tools decreases the rate of annotation errors if combined with manual quality control, it is also true that the result, even in the case of completely manual annotation, can never be 100% correct [START_REF] Leech | Chapter 2. adding linguistic annotation[END_REF]. In fact, Artificial Intelligence systems mirror human behaviour and, in the specific case of NLP systems, reflect human language, which is a strong signal of individual behaviour depending on the context. However, context is latent information in corpora, and a lack of attention to context while processing texts automatically could lead to issues in representativeness issues, such as the overgeneralisation of human behaviour [START_REF] Hovy | The social impact of natural language processing[END_REF]. [START_REF] Leech | Chapter 2. adding linguistic annotation[END_REF] states that the information recorded through the corpus annotation levels can be also extracted with wider aims, such as building dictionaries or, in the case of POS tagging annotation, building an automatic syntactic parser.

Indeed, annotation should aim to be multi-functional, especially considering that 'future uses are always more variable than the originator of the corpus could have imagined! The same is true of an annotated corpus: the annotations themselves spark off a whole new range of uses which would not have been practicable unless the corpus had been annotated.' (Leech, 2004, 23).

For the same reason, annotation should always be easily separable from the text, transparent to human readers, and explicitly documented together with the pre-annotation practices, such as text segmentation. As stated by [START_REF] Leech | Chapter 2. adding linguistic annotation[END_REF], annotation should comply with two quality criteria: the category realism of the annotation, and its accuracy and consistency. Category realism refers to the tag's capability to represent linguistic elements well. Achieving a high quality of representation in terms of tagsets is a very difficult, if not impossible, task, especially when it comes to tagsets designed to describe multiple languages. Accuracy concerns the percentage of tokens which have been correctly annotated. The information integrated in TArC is therefore as follows:

1. The metadata

Access & Ethics

One of the aims of corpus building is sharing the corpus with the scientific community, so that it can serve as a useful tool for various types of research. As already mentioned, it is important to ensure the ongoing availability of the resource, i.e. providing it though a reliable repository. In the case of TArC, as previously noted (section 2.3.2), the GitHub platform was chosen in this case. Freely accessible corpora in Open Access are not readily available for two reasons: 9

1. A corpus is a CBs intellectual property, considering their intellectual work on texts.

2. A corpus is a collection of texts produced by other people, who are the intellectual owners of this texts.

As mentioned previously, the corpus collection of DNW texts leads to ethical issues, the solution to which is not always evident, a reminder of which is provided by the 9 Collecting someone's text and putting them into a corpus can be a breach of copyright, even if the corpus is not distributed. However, in general, CBs prefer at least to be the first to be able to perform analyses on corpora, before they make them available to others. This also allows them to test them and to provide an evaluation of their results, which can then be reduplicated, once the corpus is made available to others [START_REF] Wynne | Archiving, distribution and preservation[END_REF].

National
10 A guidelines for ethic research based on Internet data is available at https: //www.forskningsetikk.no/en/guidelines/social-sciences-humanities-law-and-theology/ a-guide-to-internet-research-ethics/. Consulted on 7th April 2021.

constitutes good and justifiable research are not always obvious. [...] Researchers are personally responsible for ensuring that the protection of the interests and rights of individuals, based on the respect for human dignity and the requirement to protect privacy, are protected.'

Europe has implemented the General Data Protection Regulation (GDPR), a European Union regulation on personal data processing and privacy, which was adopted on 27 April 2016, and operational as of 25 May 2018. With this regulation, the European Commission aimed to strengthen the protection of personal data of European Union (EU) citizens and EU residents, both within and outside EU borders, by giving citizens back control of their personal data, simplifying the regulatory environment affecting international affairs, and unifying and homogenising privacy laws within the EU. However, there are no regulations dedicated to the practice of data collection for research; the responsibility for this lies in the hands of the researcher. In order to exhibit ethical behaviour, the first question the researcher must ask oneself is whether the data he or she intends to collect is public or private. It turns out that the distinction between the two is not as sharp as we might expect at first glance. In addition to the fact that some sites (usually forums) require a membership that regulates access, limiting the visibility of conversations, the expectations of the authors for their texts must also be taken into account, that is, if the user has written a text, whether they imagine their audience to be limited to users of that environment or to be open to the public without any limits, as NESH further reports: 'As a main rule, researchers ought to proceed with greater caution the stronger the restrictions on access. On the other hand, the purpose of technical restrictions on access and 'private' groups may be to protect statements that in principle are public. For example, Facebook-groups with thousands of members could be regarded as public, despite any technical settings indicating that the group is 'private' or only for 'friends'. The larger the group, the more public the information.' [START_REF] Marcoccia | Analyser la communication numérique écrite[END_REF] states that forums are usually considered public, even if the publication of a new post requires an inscription, and that easy access doesn't automatically translate to public. The researcher should also consider the content, and if it reveals personal and sensitive information in open online forums, the researcher should exercise due care and take personal responsibility to safeguard the integrity and interests of the individual, including respect for their privacy and family life. The best solution for ethical data collection is to seek written permission from users, which, in the case of large numbers of users, is not always feasible. In order to protect the users' identity, the researcher must then adopt strategies such as the anonymisation and obscuration of sensitive data.

As far as TArC is concerned, the following measures were taken: the anonymisation of all texts and the obscuration of sensitive data. Consent from the authors of the blog was also requested, with the owners signing a document, granting us the possibility to publish the corpus, with one of the two non-commercial Creative Commons licenses which we intended to use. The text of the agreement form states that:

'By signing this form, the author of the blog: Blogger name, agrees to the use that Corpus Builders names put forth, for academic research purposes.

The uses will consist of:

Deep Learning Techniques

General Background

Deep Learning (DL) is a sub-set of Machine Learning (ML), which is a sub-set of Artificial Intelligence (AI). As noticed by [START_REF] Oberlander | Cognitive science: Overview[END_REF], '[m]ost researchers in AI aim to develop computer programs that help a machine exhibit behaviour that, if it were the behaviour of a human, would be called intelligent.' Moreover, Chowdhary (2020) stated that intelligence could be defined as the result of perception + analysis + reaction. The first of these is committed to the concept of learning through real experience, while the second is committed to the capacity for identifying its structures, and the third to the ability to solve new problems. DL, being a type of ML, deals with making machines learn specific tasks through models trained on large amounts of data. What distinguishes DL from ML models, in general based on probability calculations, is mainly the neural modeling which is typical of DL. Artificial Neural Networks (ANNs) are inspired by the structure of the nervous tissue of biological brains (in fact, Deep Learning is also part of the bioinspired intelligence field). ANNs today are used for many different and disparate tasks, such as voice recognition, customer support, and image recognition, for example for medical care (MR image analysis). Regarding tasks which are more familiar in NLP, we can mention machine translation, language modelling, coreference resolution, sequence tagging, syntactic analysis, etc.. Artificial intelligence can be traced back to the 1940s, when mathematicians formalised the neuron as a small processor. In particular, McCulloch & Pitts (1943) observed that networks of binary neurons could perform logical operations, by activating or not activating connections between them, like Boolean circuits. The conclusion of their observations was that the brain is a logical inference machine, because neurons are binary, so a neuron computes a weighted sum of its inputs and then compares the weighted sum to a threshold [START_REF] Russel | Artificial intelligence: a modern approach[END_REF].

The idea that the brain learns by modifying the strength of connections between neurons, which are called synapses, is formalised in the Hebbian theory of synaptic plasticity, which owes its name to Donald Hebb, a Canadian psychologist. Donald Hebb demonstrated, in 1949, a simple update rule for changing the connecting forces between neurons. His rule, now referred to as Hebbian learning, still remains an influential model these days. [START_REF] Russel | Artificial intelligence: a modern approach[END_REF] on the minimisation of an error, i.e. the so-called error back-propagation function. This function, which is still used today, alters the weights of the connections (synapses), taking into account the difference between the actual output and the desired one.12 The enthusiasm was great, but after [START_REF] Minsky | Perceptrons: an introduction to computational geometry[END_REF] demonstrated the limits of the perceptron, i.e. its ability to recognise, after appropriate training, only linearly separable data, interest waned rapidly. In fact, a multi-level network of perceptrons could solve more complex problems, but the increasing computational complexity of training made this road impractical. It was only in the following decade that the usefulness of this operational entity began to be considered again, especially in terms of multi-layer neural networks. However, if artificial intelligence began around the 1940s, it is only now that it is undergoing exponential acceleration thanks to the power of current technological tools compared to those of the past. Being more powerful, they can analyse more data at the same time, and the resulting programs can also be more effective. The explosion of data and computing power in the 2000s brought these techniques back to the forefront, due to the fact that it is now possible to build much deeper networks with many layers, hence deep networks. An important milestone in NLP was the work of [START_REF] Bengio | A neural probabilistic language model[END_REF][START_REF] Bengio | A neural probabilistic language model[END_REF]. In fact, they definitely made real improvements by proposing a neural probabilistic language model which can simultaneously learn a distributed word representation (word feature vector) and the probability function for word sequences (in terms of word feature vectors of the sequence words).13 A few years later, [START_REF] Schwenk | Continuous space language models[END_REF] extended the previous work to language modeling for large vocabulary continuous speech recognition. Indeed, NLP research during this period experienced a huge surge. Android has been using neural net speech recognition systems since 2012. However, 2013 was the year in which computer vision started to uses neural nets and gradually abandoned all other techniques. In addition, Google's and Facebook's current NLP applications are completely built around Deep Learning.

Neural Networks structure can be described, in terms of structure, as a model consisting of at least two layers, an input layer and an output layer, and there are usually also additional intermediate layers (hidden layers). Each layer of the network contains a number of specialised artificial neurons. Information in the form of patterns or signals is transferred to neurons in the input layer, where it is then processed. Each neuron is assigned a weight, so that each one receives a different relevance. The weight, along with a transfer function, determines where the information is then forwarded. In the next step, an activation function using a threshold value calculates and weights the output value of the neuron. Depending on the information evaluation and weighting, other neurons are connected and activated to a greater or lesser extent. By means of these processes, a model is set up to produce an output for each input. With each training process, the weighting, and thus the algorithm's parameters, is modified so that the network yields more accurate and better results. Different neural network structures are used depending on the learning method used and the purpose of the application, such as Feed-Forward Networks (FFNs), Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). Learning can occur either in a supervised or unsupervised form. The difference between the two is that supervised learning allows the model to make inferences from data that has already been labeled and is used for classification and regression tasks. Unsupervised learning occurs when the model learns from an unlabeled training set, and thus requires a self-organisation of the net parameters, based on training data set characteristics.14

Overview of Natural Language Processing Approaches

'Natural Language processing [...] is the sub-field of artificial intelligence (AI) focused on modelling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA) [...] etc. NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others' [START_REF] Darwish | A panoramic survey of natural language processing in the arab world[END_REF].

The neural network revolution and the birth of Deep Learning was hinted in the previous section. This revolution has also disrupted the field of Natural Language Processing (NLP). In fact, nowadays, NLP is embracing AI techniques, and can thus be considered to be an AI sub-field, as stated by [START_REF] Darwish | A panoramic survey of natural language processing in the arab world[END_REF] in the above quote. However, before the DL upheaval, NLP was also addressed using other available approaches, such as the symbolic one, based on grammaratical complex hand-written rules with the aim of parsing the language. For further details on this approach, refer to the work of [START_REF] Charniak | The Frail/NASL reference manual[END_REF]; [START_REF] Hirst | A semantic process for syntactic disambiguation[END_REF]; [START_REF] Riesbeck | From conceptual analyzer to direct memory access parsing: An overview[END_REF]; [START_REF] Hirst | Semantic interpretation and the resolution of ambiguity[END_REF]; [START_REF] Grosz | Team: An experiment in the design of transportable natural-language interfaces[END_REF]. An important development of symbolic approaches in NLP has been the employment of statistical models. In fact, statistical models have made it possible to overcome the limitations of the complexity of hand-written rules by allowing them to be generated through machine learning. For more information about the statistical approach to Natural Language Processing, please refer to [START_REF] Lesk | Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone[END_REF]; [START_REF] Bahl | A treebased statistical language model for natural language speech recognition[END_REF]; [START_REF] Brown | A statistical approach to machine translation[END_REF]; [START_REF] Chitrao | Statistical parsing of messages[END_REF]; [START_REF] Brill | Deducing linguistic structure from the statistics of large corpora[END_REF][START_REF] Brown | Word-sense disambiguation using statistical methods[END_REF]; [START_REF] Liberman | The trend towards statistical models in natural language processing[END_REF].

The current popular method for representing words in Deep Learning, which has produced impressive results, consists of using word embedding, namely word representation in a continuous multidimensional space. In fact, most of the DL approaches require an input encoded as a vector of features of fixed dimensions. The idea of using neural networks that represent words in a continuous space to reduce the number of parameters to be estimated comes from [START_REF] Bengio | A neural probabilistic language model[END_REF]. Exploiting the fact that words have multiple degrees of similarities, this distributed semantic representation is used to map words in a vector's continuous spaces where words take place according to their features [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF]. In order to use word embedding with Recurrent the output.

Neural Networks (RNNs), it is usually necessary to create an embedding layer in the RNN as in [START_REF] Mikolov | Language modeling for speech recognition in czech[END_REF] or [START_REF] Mikolov | Neural network based language models for highly inflective languages[END_REF]. Word embedding comprises a dense representation of words as multidimensional vectors, and the vector dimensions vary according to the amount of vocabulary. One way to create word embedding is to start with dense vectors for each token containing random numbers, and then use these to train a model such as a document classifier or sentiment classifier. The vectors, which represent the tokens, will be distributed in such a way that semantically closer words will have similar representations. However, in order to use word embedding, it is also possible to employ the most common models such as Word2Vec [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] or GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF]. This approach has been widely adopted, especially in light of improvements with respect to vector quality and model training speed, and advances in hardware have also been made. In general, DL-based approaches allow for the production of results that highly depend on the textual input data, which can be very large. Unlike with the previously mentioned approaches, the more observations the DL algorithm encounters, the more it will improve and gain in precision.

Indeed, in the last decade, neural networks have established themselves as the most state-of-the-art models in most NLP tasks. This can be also explained by the fact that textual content is becoming more available on social media and sizable corpora can be collected and used more easily. Data is, in fact, the lymph of NLP, and this is true for all rule-based approaches that require lexicons and carefully created rules, as well as for DL supervised approaches that require corpora, especially annotated corpora. The next section (namely section 2.4.3) briefly mentions sequence-to-sequence models [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF][START_REF] Vaswani | Attention is all you need[END_REF], which are built on top of Recurrent Neural Networks (RNN) (Hochreiter and Schmidhuber, 1997b;Cho et al., 2014a), and attention mechanism [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF][START_REF] Vaswani | Attention is all you need[END_REF] structured in encoder-decoder architectures, these are currently among the most effective solutions for NLP problems.

Sequence Modelling

Sequential data prediction is considered by many as a key problem in machine learning and artificial intelligence [START_REF] Mikolov | Recurrent neural network based language model[END_REF]. There are several Deep Learning (DL) models used for data processing, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and in particular LSTM, etc. These models are different from each other, and each of them is known to be particularly proficient at performing certain specific tasks, although, basically, they all share a similar architecture: an input layer, an output layer, and one, two (shallow NN ) or more hidden layers (deep NN ) in between same. Information propagates through layers, thanks to the connections between them. The method of connection varies for different networks.

As already mentioned at the beginning of this section (2.4.1), the Feed Forward Network (FFN) 'perceptron' was the first and simplest type of artificial neural network devised. A basic idea of the FFN structure is given in figure 2.1, where x represent the input values and y is the output. The arrows are the weight matrices that connect the layers of the represented shallow net. The name 'Feed Forward' comes from the direction of the information through this kind of net, which is always forward. Otherwise, in the Recurent Networks (RNNs), a recurrent matrix connects the hidden layer to itself, using time-delayed connections, as shown in figure 2.2. In the RNN unfolding graph in figure 2.2, each node is associated with one specific time step.

The RNN chunk represented in the figure as (R) receives one input x, and outputs only a single hidden state h With respect to sequence modelling, some of today's applications include speech recognition, machine translation, topic extraction (i.e. keyphrase lists extraction), speech generation, or text summarising of customer feedback. Indeed, sequences are a data structure in which each example can be viewed as a series of data points. In order to model sequences, a specific framework that can handle sequences of varying lengths is required, while also maintaining their order, continuing to track long-term dependencies, and sharing parameters across the sequence. Therefore, language models based on RNNs have been proposed to overcome some limitations of FFN networks, such as their inability to detect dependencies between input patterns [START_REF] Bengio | Scaling learning algorithms towards ai[END_REF][START_REF] Mikolov | Recurrent neural network based language model[END_REF]. In fact, RNNs can efficiently represent complex patterns thanks to their (already mentioned) time-delayed connections (unlike FFN that operate at a single time). The time-delayed connection, which is represented by the blue arrows in 2.2, allow the recurrent model to form a kind of short-term memory, since past information can be represented by the state of the hidden layer that is updated based on the current input and the state of the hidden layer in the previous time step [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF]. Finally, at each time step (t), a RNN produces an output based on the current input (x t ) and the previous state (h t-1 ). An output, to which an error corresponds, is produced at each time step. For this reason, the error back-propagation in RNNs must involve all the time steps of the net. The back-propagation method, in order to automate the updating of weights in the network hidden state, is based on the gradient descent technique, and is used to correct each parameter of the model by comparing its contribution to the error on the given output (already known).

t . 15 R R R R = R h 0 x 0 h 1 x 1 h 2 x 2 h t x t h t x t . . .
However, as pointed out by [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF], even if the RNN was provided with all the relevant information, it would still be considered difficult to train it, due to the long-term dependencies involved therein which may cause gradient explosion or vanishing problems. Therefore, in order to solve this type of problem, it has been proposed to use a particular recurrent network, i.e. the Long Short-Term Memory (LSTM), which was introduced by Hochreiter and Schmidhuber (1997a). As discussed above, a RNN has a hidden unit that computes a function of an input and its own previous output, and this is also known as the cell state or hidden state. The difference between a traditional RNN and LSTM resides in the LSTM gating system. The latter allows for control of the flow of information when entering or exiting from the cell module. The gate control has been described as enabling better storage of 'memory'.

Furthermore, the cell module can maintain its hidden state through time and process data sequentially by adding or removing information thanks to four additional layers inside it. These additional layers for a traditional LSTM include the 'forget gate layer'

(σ 1 in figure 2.3), which is a layer tasked with keeping or removing information. The second one is a layer known as the 'input gate layer' (σ 2 in figure 2.3) for selecting the values to be updated and transferring the information to the following Tanh layer, which produces a set of new possible values for the update.16

σ 1 σ 2 Tanh σ × + × × Tanh c t-1 Cell h t-1 Hidden xt Input ct ht ht Figure 2.3: Long Short-Term Memory (LSTM) cell.
The LSTM cell is composed of another gate layer, i.e. the 'output' layer. This layer has the function of filtering the information that will be comprised in the new hidden state to be output. First of all, the current cell state has to be squashed through a new Tahn function. Then, the filter vector is created by the passage of the previous hidden state and the current input data through a sigmoid function. Finally, the squashed cell state is multiplied by the filter vector, giving the final new hidden state to be output.

A hidden unit similar to the LSTM hidden cell, but at its simplest, has been proposed by Cho et al. (2014b), and is known as the Gate Recurrent Unit (GRU). In comparison to LSTM, it is considered to be much simpler, because it has less parameters than the LSTM cell, even if the idea is relatively similar. In order to handle previous information together with the next information, [START_REF] Schuster | Bidirectional recurrent neural networks[END_REF] proposed bidirectional RNNs sequence models.

Considering the improvements given with RNN-based architectures in mapping sequences (Dinarelli and Grobol, 2019c;[START_REF] Dinarelli | Modélisation de dépendances entre étiquettes dans les réseaux neuronaux récurrents[END_REF][START_REF] Dupont | Réseaux neuronaux profonds pour l'étiquetage de séquences (deep neural networks for sequence labeling)[END_REF], the first sequence-to-sequence architecture, proposed by Cho et al. (2014b), was a strategy based on input-encoding into a fixed-length vector using RNNs, and outputdecoding into the target sequence with another RNN. This architecture, referred to by Cho et al. (2014b) as the RNN's encoder-decoder architecture, is also referred to as sequence-to-sequence in the similar work proposed by [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF].

x Encoder c Decoder y An encoder-decoder or sequence-to-sequence RNN architecture is thus employed to generate an output sequence (y 1 ...,y (ny) ), given an input sequence (x 1 , x 2 ...,x (nx) ).

The last hidden state of the RNN-encoder is employed to compute a generally fixedsize context variable (C ). This consists of an input sequence semantic summary and is passed, as an input, to the RNN-decoder (Goodfellow et al., 2016, 396-397).

As pointed out by [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF], the limitation of sequence-to-sequence architecture becomes clear when the context (C ) output by the encoder RNN has a dimension that is too small to properly summarise a long sequence (Goodfellow et al., 2016, 397). Among the various proposals suggested to overcome this limitation, one consisted of an attention mechanism [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF]. This is a mechanism used to focus on specific parts of the input sequence C vector, which are more relevant for output prediction (Goodfellow et al., 2016, 475-476). In fact, in sequence-to-sequence models, the C vector of the input sequence, which is used as a fixed-length vector, that, as previously stated, has strong limitations when handling long sequences, always remains the same, regardless of the decoder's hidden state. An image of the attention mechanism is given in figure 2.5. The central idea behind the attention mechanism is to overcome this problem by keeping the active focus on intermediate encoder hidden states, instead of using only the final states to initialise the decoder.

Decoder Attention layer

Encoder

C vector After encoding the input sequence into a set of internal states (h 1 ...h n ), a score for each one (s 1 ...s n ) is computed by a FFN, in order to focus solely on the most relevant one. The FFN is trained to identify the relevant states and assign a high score to them, and low scoring internal states will be ignored. Using the generated scores, attention weights (a 1 ...a n ) are then computed by a softmax layer [START_REF] Bridle | Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition[END_REF]. As a logistic function, this squashes values between 0 and 1 and makes sure that weights add up to 1. Finally, the context vector (C ) is computed, and this will be used by the decoder for prediction of the next word in the sequence. The computation simply consists of multiplying each attention weight by the corresponding internal state (i.e.: a 1 * h 1 ) and summing all the results. The decoder will thus be provided with the concatenation of the C vector with the output of the previous step. Therefore, while being of a fixed size, C is computed dynamically for every prediction. In this way, the decoder will not only receive information about all the sequences (C vector), but also about the internal states which are considered to be more relevant at the specific step by the FFN. The attention mechanism is so powerful that it allows models, not only to perform better, but also to train faster. The only unsolved issue in RNN, regardless of whether an attention mechanism is used or not, is its difficulty in parallelisation, with the next hidden states depending on the previous ones. In order to solve this problem, Google Brain team members came up with the Transformer model which uses only attention and gets rid of all the other layers, including CNN or RNN, thus making it highly parallelisable, inherently bidirectional, and able to perform efficient computation [START_REF] Vaswani | Attention is all you need[END_REF].

h dec a 1 a 2 a 3 s 1 s 2 s 3 h 1 h 2 h 3 x 1 x 2 x 3

Multi-tasking

A multi-tasking learning approach, as a set of learning algorithms, makes what is learned when performing a task transferable, in order to improve the performance of a similar task. This approach emphasises learning multiple tasks, in a parallel or cascaded manner, that are considered to be relevant by sharing the representation of information at intermediate layers [START_REF] Caruana | Multitask learning[END_REF]. This approach, that is employing neural networks since early in their development and diffusion (Collobert andWeston, 2007, 2008;[START_REF] Collobert | Natural language processing (almost) from scratch[END_REF], has been proven to be particularly beneficial for ambiguous data, considering its ability to reduce sparsity, helping to process complex patterns which involve multiple features. This is the case, for example, of POS tagging [START_REF] Rush | Improved parsing and pos tagging using inter-sentence consistency constraints[END_REF][START_REF] Søgaard | Deep multi-task learning with low level tasks supervised at lower layers[END_REF]Alonso and Plank, 2016;[START_REF] Bingel | Identifying beneficial task relations for multitask learning in deep neural networks[END_REF][START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple nlp tasks[END_REF]. This is particularly relevant to the morphological richness of Arabic, as addressed by [START_REF] Inoue | Joint prediction of morphosyntactic categories for fine-grained arabic part-of-speech tagging exploiting tag dictionary information[END_REF], or dialectal Arabic, i.e. in Zalmout and Habash (2019a). As an example, the multi-task architecture proposed in [START_REF] Collobert | A unified architecture for natural language processing: Deep neural networks with multitask learning[END_REF] is a deep neural network that is trained jointly for all the tasks that it is required to perform. In such instances of joint learning, the information processed by deep layers, as well as the features which are trained for one task, are shared across the net, as both can be useful for other related tasks. Therefore, when training NNs on related tasks, sharing what is learned by different layers during joint training improves the generalisation performance, while the last layers of the network may be task-specific [START_REF] Collobert | A unified architecture for natural language processing: Deep neural networks with multitask learning[END_REF]. Another example of multi-task learning involving joint training and shared parameters is that proposed by Zalmout and Habash (2019a). Their proposal consists of a multi-task learning for joint modelling of MSA and Egyptian dialect morphological tagging with adversarial training. 17

They used a Bi-LSTM tagging model, with shared parameters of the hidden layers, to jointly learn different morphological features. In addition, the input (words and characters) representations for both MSA and Egyptian were shared. However, this joint embedding space did not perform well. Furthermore, they also used a unified feature-17 This is a transfer-learning method used for domain-adaptation, in which the adversarial training extracts invariant features from the source domain and applies the extracted invariant features to boosting the model performance on the target domain. In Zalmout and Habash (2019a), the source domain is in both MSA and Egyptian, and the authors used this method to learn the common features between the two systems in order to transfer knowledge from the highest resource (MSA) to the lower resourced system (Egyptian), which are both modelled in the same invariant space.

tag vector representation on all features, through concatenating all the feature-vectors (v-gender + v-number etc.) for each word in a unique vector, similarly to [START_REF] Inoue | Joint prediction of morphosyntactic categories for fine-grained arabic part-of-speech tagging exploiting tag dictionary information[END_REF]. Furthermore, [START_REF] Collobert | A unified architecture for natural language processing: Deep neural networks with multitask learning[END_REF] states that: 'If one possesses a dataset labeled for several tasks, it is possible to train these tasks jointly in a shallow manner: one unique model can predict all task labels at the same time.' However, this is not the case for Tunisian, which, as will be explored in more depth in the next section (section 2.5), is a low-resourced system, and does not have a large amount of available corpora in order to develop this kind of joint learning. Nevertheless, assuming that we need to produce mutually related levels of text annotation, such as tokenisation and POS tagging, the most intuitive way to produce this information is in a sequential order. Tokenisation and POS tagging are two exact annotation levels as in our corpus. Considering the intuitive sequential order used to organise the different tasks in a cascade, tokenisation is produced as a first level and the hidden representation of the tokenisation module is used as an input for the following one, which, in the case seen in this example, is POS tagging. In this way, each task is performed interdependently, but at the same time, each architecture layer receives the information produced by the previous layers (this topic will be more deeply reflected on in section 2. 4.4). A path involving the use of a multi-task architecture was also investigated by [START_REF] Meftah | Multi-task supervised pretraining for neural domain adaptation[END_REF], who proposed an interesting approach based on sequential transfer learning and multi-task learning, with pre-training occurring on a resource-rich domain and fine-tuning on a low-resourced domain in a multi-task modality. In view of the importance of the dataset structure for this type of model [START_REF] Collobert | A unified architecture for natural language processing: Deep neural networks with multitask learning[END_REF], the next section features records regarding the situation for Arabic processing, in particular for dialectal Arabic. The focus of this will be the already developed and available corpora for dialectal Arabic, and in particular for Tunisian. Finally, we will outline the available corpora for Tunisian encoded in both the Arabic and Latin scripts.

State of the Art

Arabic Natural Language Processing

As discussed in section 2.4, Natural Language Processing is a highly interdisciplinary field with links to computer science, linguistics, cognitive science, psychology, mathematics, etc. Data is the lifeblood of NLP: this is true for rule-based approaches that require carefully crafted lexicons and rules, but also for machine learning approaches that require corpora and specially annotated corpora. As a Semitic language, Arabic has a rich inflectional and derivational morphology, which makes processing Arabic a compelling challenge. This morphological complexity in early automatic Arabic processing was often handled through morphological analysers, such as BAMA [START_REF] Buckwalter | Buckwalter Arabic morphological analyzer version 1.0. linguistic data consortium[END_REF][START_REF] Buckwalter | Buckwalter Arabic morphological analyzer (bama) version 2.0. linguistic data consortium (ldc) catalogue number ldc2004l02[END_REF]. Recently, there has been a substantial increase in the amount of NLP research for morphological analysis, disambiguation, Part-of-Speech (POS) tagging, and lemmatisation for both Standard Arabic (MSA) and Dialectal Arabic (DA) (Gugliotta et al., 2020). [START_REF] Darwish | A panoramic survey of natural language processing in the arab world[END_REF] notes that automatic processing of Arabic has undergone three waves: the rule-based phase (since the early 1980's), the period of US funding for major ANLP projects (since September 11), and the Arab World ANLP (since 2010). Large projects have been funded for companies and research centers with the aim of developing NLP tools for Arabic and its dialects, and these have began to gradually shift away from rule-based approaches. As already illustrated in section 2.4, Machine Learning (ML) requires much less language knowledge and is both faster and more accurate. However, ML requires a lot of data, which is not always easy to collect (i.e. for parallel texts in dialectal Arabic). The second wave has begun to yield early success in examples of blended systems (such as a combination of rule-based morphological parsers and ML disambiguation), such as the Penn Arabic Treebank (PATB) [START_REF] Habash | Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop[END_REF][START_REF] Maamouri | The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus[END_REF], or MADA+TOKAN, which is a toolkit for Arabic tokenisation, diacritisation, morphological disambiguation, POS tagging, stemming and lemmatisation (Habash et al., 2009). The period that began around 2010 observed exponential growth in ANLP research originating in the Arab world. According to [START_REF] Darwish | A panoramic survey of natural language processing in the arab world[END_REF], this period also overlapped with two important independent developments: 1. The rise of Deep Learning and neural models; and 2. The development of social media, which led to the increase in available data for research. As an example, [START_REF] Zalmout | Don't throw those morphological analyzers away just yet: Neural morphological disambiguation for Arabic[END_REF] presented a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). The authors based their work on the Penn Arabic Treebank and used LSTM by showing that this model has a significant performance. In addition, the success of word embedding [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF][START_REF] Pennington | Glove: Global vectors for word representation[END_REF] being trained on non-annotated data and the resulting improved performance for NLP tasks has also contributed to the growth of interest in ANLP (Al Sallab et al., 2015;[START_REF] Farha | Mazajak: An online Arabic sentiment analyser[END_REF][START_REF] Soliman | Aravec: A set of arabic word embedding models for use in arabic nlp[END_REF]. 18 Table 2.8 reports some of the most recent and well-known tools in the field of automatic processing of MSA morpho-syntax.

However, as mentioned just above, in order to train Deep Learning models to perform tasks efficiently, a large amount of structured data is required. Building up resources is a long and expensive task that requires significant work over long periods of time. As seen in section 2.3, corpus building is often tackled by teams of linguists or lexicographers. NLP relies heavily on the existence of corpora to develop and evaluate its models, and the performance of NLP applications depends directly on the quality of these corpora [START_REF] Darwish | A panoramic survey of natural language processing in the arab world[END_REF]. MSA undoubtedly has an advantage over dialectal Arabic in terms of data collection, due to the large amount of data presented online, even before the emergence of social networks. In fact, MSA is used to write newspaper articles, which have been used to build MSA corpora in some cases, as in [START_REF] Selab | Building talaa, a free general and categorized Arabic corpus[END_REF] and El-Haj and Koulali (2013). Corpora do not necessarily need to have annotations, as in the case of the ArTenTen corpus [START_REF] Arts | artenten: Arabic corpus and word sketches[END_REF], the LDC's Arabic Gigaword [START_REF] Parker | Arabic gigaword fifth edition ldc2011t11[END_REF] or the Qatar Arabic Language Bank (QALB), which was built with the aim of supporting automatic correction of MSA spelling [START_REF] Habash | Qalb: Qatar Arabic language bank[END_REF]. On the other hand, the most notable annotated resources include the LDC's Penn Arabic Treebank (PATB), which provides a relatively large MSA corpus that is morphologically analysed, segmented, lemmatised, tagged with fine-grained parts of speech, diacritised and parsed [START_REF] Maamouri | The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus[END_REF], the Prague Arabic Dependency Treebank (PADT), which was the first dependency representation for Arabic [START_REF] Smrz | Prague dependency treebank for Arabic: Multi-level annotation of Arabic corpus[END_REF], and the Columbia Arabic Treebank (CATiB) [START_REF] Taji | An Arabic dependency treebank in the travel domain[END_REF]Habash and Roth, 2009). The latter was actually an effort to develop a simplified dependency representation with a faster annotation scheme for MSA. As such, it consists of a small dependency treebank of travel domain sentences in MSA, created by translating 2000 selected sentences from the Basic Travelling Expression Corpus (BTEC) [START_REF] Takezawa | Multilingual spoken language corpus development for communication research[END_REF] 2013). Regarding the construction of MSA corpora, further efforts have been devoted

to the construction of parallel corpora, including the United Nations Parallel Corpus [START_REF] Ziemski | The united nations parallel corpus v1. 0[END_REF], where MSA documents are aligned to their corresponding version in Chinese, English, French, Russian and Spanish, an Arabic-Japanese corpus, which was manually aligned at sentence level [START_REF] Inoue | A parallel corpus of Arabic-japanese news articles[END_REF], and a subtitled parallel corpus covering 60 languages including MSA, which was a release of an extended version of the OpenSubtitles collection of parallel corpora (Lison and Tiedemann, 2016). 19 The BTEC is a multilingual spoken language corpus containing tourism-related sentences similar to those that are usually found in phrasebooks for tourists going abroad.

Processing of Arabic Dialects

With the new availability of easily accessible dialectal Arabic data, there is growing interest in applying ANLP to dialectal Arabic processing (Al-Sabbagh and Girju, 2012;[START_REF] Bouamor | A Multidialectal Parallel Corpus of Arabic[END_REF][START_REF] Bouamor | The madar arabic dialect corpus and lexicon[END_REF][START_REF] Diab | Colaba: Arabic dialect annotation and processing[END_REF][START_REF] El-Haj | Habibi -a multi dialect multi national Arabic song lyrics corpus[END_REF][START_REF] Gadalla | Callhome egyptian Arabic transcripts ldc97t19[END_REF]Sadat et al., 2014a;[START_REF] Salama | YouDACC: the Youtube dialectal Arabic comment corpus[END_REF][START_REF] Harrat | Building resources for algerian arabic dialects[END_REF]. In particular, when narrowing the field to NLP work applied to DA, two main macro-strategies, which are aimed at overcoming the lack of data for DA, can be observed: 1. The adaptation of MSA systems to DA processing, such as [START_REF] David | Parsing arabic dialects[END_REF], who exploited the Penn Arabic Treebank [START_REF] Maamouri | The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus[END_REF] and used explicit knowledge about the relationship between MSA and Levantine Arabic. In addition, [START_REF] Duh | Pos tagging of dialectal Arabic: a minimally supervised approach[END_REF] constructed a POS tagger for Egyptian through a minimally supervised approach by using the CallHome Egyptian Colloquial Arabic Speech (ECA) corpus [START_REF] Canavan | CALLHOME Egyptian Arabic Speech LDC97S45[END_REF]. Regarding dialectal Arabic parsing, [START_REF] David | Parsing arabic dialects[END_REF] addressed the problem of parsing transcribed spoken Levantine Arabic (LA) using explicit knowledge about the relationship between LA and MSA. 2. The constitution of new resources not based on MSA-DA relations, as lexicons, corpora or dialectal NLP systems.

Regarding lexicons, the Algerian lexicon of Abidi and Smaïli (2018), or the Moroccan one, built by [START_REF] Tachicart | Building a moroccan dialect electronic dictionary (mded)[END_REF], have already been mentioned. Concerning corpora, there are different types, including parallel corpora, raw single-variety corpora, annotated corpora or treebanks, such as the LDC's Levantine and Egyptian Arabic Treebanks [START_REF] Maamouri | Developing an egyptian Arabic treebank: Impact of dialectal morphology on annotation and tool development[END_REF]. There is also a distinction between written-based corpora and speech-based, such as Fisher Levantine Arabic Conversational Telephone Speech [START_REF] Maamouri | Fisher levantine arabic conversational telephone speech[END_REF]. In contrast, the Levantine Dialect Corpus (Shami), is a written-base corpus covering the dialects of Palestine, Jordan, Lebanon and Syria and containing 117,805 Twitter sentences [START_REF] Kwaik | Shami: A corpus of levantine arabic dialects[END_REF]. Shami is not annotated, while Curras is a Palestinian Arabic annotated corpus [START_REF] Jarrar | Curras: an annotated corpus for the palestinian Arabic dialect[END_REF] consisting of approximately 56,000 tokens. It presents both lexical and morphological manually checked rich annotation. When building same, the authors employed MADAMIRA [START_REF] Pasha | Madamira: A fast, comprehensive tool for morphological analysis and disambiguation of arabic[END_REF], as did the authors of SUAR, a semi-automatically annotated Saudi corpus, which contains 104,079 words. Moreover, in this case, the annotation had been manually checked [START_REF] Al-Twairesh | Suar: Towards building a corpus for the saudi dialect[END_REF]. By exploiting web data, Alsarsour et al.

(2018) built the manually-annotated Dialectal Arabic Tweets data set (DART). This corpus is a collection of approximately 25,000 tweets annotated via crowd-sourcing. [START_REF] Diab | Colaba: Arabic dialect annotation and processing[END_REF] built COLABA, an Arabic corpus that was built for NLP resources covering four Arabic dialects: Egyptian, Iraqi, Levantine, and Moroccan. The authors utilised MAGEAD [START_REF] Habash | Magead: A morphological analyzer and generator for the Arabic dialects[END_REF] and the Buckwalter morphological analyser and generator for MSA and DA [START_REF] Buckwalter | Buckwalter Arabic morphological analyzer (bama) version 2.0. linguistic data consortium (ldc) catalogue number ldc2004l02[END_REF]. The COLABA corpus is part of the COLABA project objectives, namely:

'an initiative to process Arabic social media data such as blogs, discussion forums, chats, etc. Given that the language of such social media is typically DA, one of the main objective of COLABA is to illustrate the significant impact of the use of dedicated resources for the processing of DA on NLP applications.' [START_REF] Diab | Colaba: Arabic dialect annotation and processing[END_REF].

The objectives of dialectal corpus building include that of developing dedicated tools for a specific variety, such as the Columbia Arabic Language and Dialect Morphological Analyser (CALIMA) for the Egyptian dialect [START_REF] Habash | A morphological analyzer for egyptian Arabic[END_REF]. For the same dialectal variety, [START_REF] Darwish | Multi-dialect arabic pos tagging: A crf approach[END_REF] proposed a POS tagger based on a Conditional Random Fields (CRF) sequence labeller. 20 The system works also on Levantine, Maghrebi and Gulf dialects. Concerning the Gulf Arabic, [START_REF] Khalifa | A morphological analyzer for gulf Arabic verbs[END_REF] built a morphological analyser covering over 2600 verbs, while [START_REF] Khalifa | Morphological analysis and disambiguation for gulf Arabic: The interplay between resources and methods[END_REF] proposed a full morphological analysis and disambiguation system for Gulf Arabic based on a morphologically annotated corpus of Emirati Arabic [START_REF] Khalifa | A morphologically annotated corpus of emirati Arabic[END_REF]. The authors employed 200,000 words selected from the Gumar corpus (Khalifa et al., 2016a), while for the annotation, they used the MADARi interface [START_REF] Obeid | Madari: A web interface for joint Arabic morphological annotation and spelling correction[END_REF] Considering that the main source of dialectal Arabic data for corpora building is the world wide web, and that both MSA and the various dialects use the same encoding systems in Arabic or in Latin characters, one of the main problems addressed by ANLP is the Dialect Identification (DI), namely, the creation of models for the recognition of dialect, or even regional varieties. 22 In general, these models are trained on multi-dialectal corpora. [START_REF] El-Haj | Arabic dialect identification in the context of bivalency and code-switching[END_REF] presented a Machine Learning approach to automatically detecting dialects, which was trained through the Arabic On-line Commentary (AOC) data set, comprising four Arabic dialects groups (Egyptian, Gulf, Levant and North African), in addition to MSA [START_REF] Zaidan | The arabic online commentary dataset: an annotated dataset of informal arabic with high dialectal content[END_REF]. In order to use bivalency and written code-switching as features in the classification process, they created dialect-specific frequency lists to distinguish the vocabularies spoken in each dialect compared to MSA. The authors of AOC data set have been annotating it, via crowd-sourcing, for four years, in order to use it for training a sentence-level DI system, which is an automatic classifier relying on n-gram language models [START_REF] Zaidan | Arabic dialect identification[END_REF]. In addition, Elfardy and Diab (2013) focused on sentence-level DI of Egyptian and MSA and proposed a supervised approach combining token-level DI and other features used to train a generative classifier that predicts the given sentence class. The year before this, the same authors proposed AIDA, a system for dialect identification, classification and glossing on the token and sentence level (Elfardy and Diab, 2012a). On the other hand, Elfardy and Diab (2012b) also presented a set of guidelines for token-level identification of DA. In fact, the same authors, in (Elfardy and Diab, 2012c;Elfardy et al., 2013), further addressed the problem of token-level dialect-identification by casting it as a code-switching problem. [START_REF] Elfardy | Aida: Identifying code switching in informal arabic text[END_REF] then presented the latest version of their system for identifying linguistic code-switching in Arabic text. The system relies on probabilistic language models and a tool for morphological analysis and disambiguation for Arabic to identify the class of each word in a given sentence. In the same year, [START_REF] Darwish | Verifiably effective arabic dialect identification[END_REF] also showed that targeting words that exhibit distinguishing traits is essential for proper dialect identification.

Indeed, the authors identified lexical, morphological, phonological, and syntactic fea- Regarding the construction of a dialectal Arabic corpora, first of all, the 'problem' of non-standardised encoding affects all Arabic dialects with different degrees of conventionalisation of the systems. This problem was addressed by Habash et al. (2012a) by presenting the Conventional Orthography for Dialectal Arabic. This is a set of guidelines, initially dedicated only to the Egyptian dialect, but later extended to other dialects, such as Algerian [START_REF] Saadane | A conventional orthography for algerian Arabic[END_REF], Tunisian [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF], Maghrebi [START_REF] Turki | A conventional orthography for maghrebi arabic[END_REF] or Gulf Arabic (Khalifa et al., 2016a). Finally, [START_REF] Habash | Unified guidelines and resources for Arabic dialect orthography[END_REF] proposed CODA Star, which acts as a common set of guidelines with enough specificity to help create dialect-specific conventions. A second matter for consideration, while building a dialectal Arabic corpora, is the need for guidelines for their annotations, an issue addressed, for example, by [START_REF] Habash | Guidelines for annotation of Arabic dialectness[END_REF], Zaghouani and Charfi (2018b) and Elfardy and Diab (2012b). The first of these works had the aim of producing the basis for the annotation of data collection suitable for dialect identification and ANLP, highlighting dialect switching between MSA and DA. The dialectness (collocating each word in a continuum ranging from 0 to 4, where 0 is pure MSA) of each word is annotated, and relies on lexemes and inflectional morphemes. In terms of the second of these works, the authors have presented an annotation pipeline.

The guidelines that they wrote for creating a large manually annotated Arabic author profiling data set from various social media sources (Zaghouani and Charfi, 2018a) will be presented below. The third work exhibits a simplified set of guidelines for detecting MSA-DA code-switching and foreign words on the word and token level. 24 The MADAR corpus, which is a parallel of 25 Arab-city dialects in addition to the preexisting parallel set for English, French and Modern Standard Arabic (MSA), built by [START_REF] Bouamor | The madar arabic dialect corpus and lexicon[END_REF] has already been briefly mentioned. This corpus was created by translating, 2000 French and English sentences, selected from the Basic Traveling Expression Corpus (BTEC) [START_REF] Takezawa | Multilingual spoken language corpus development for communication research[END_REF], into the different dialects. In addition to the 2000-sentence strong corpus (CORPUS-25), the authors also selected 10,000 other sentences representing five cities: Doha, Beirut, Cairo, Tunis and Rabat (CORPUS-6). The translators, from the Ramitechs company,25 started from French or English, and were asked to use Arabic script, avoid any code-switching and to be internally consistent in spelling words. They were not provided with any orthographic guidelines. 26As noted on several previous occasions, the arrival of social media has allowed researchers to build corpora, extracting data directly from the Internet. There are many dialectal-Arabic corpora based on web data. In tables 2.9 and 2.10, we report some of the best-known Arabic-encoded corpora, excluding Tunisian corpora, that will be addressed separately. While ANLP researchers are making strong efforts in building DA corpora, Arabic dialects still lack fine-quality word embedding. As already stated in section 2.4, word embedding is crucial to many ANLP tasks. However, this relies on large volumes of annotated and non-noisy data. 2017). These corpora are outlined in tables 2.9 and 2.10. As has emerged from tables 2.9 and 2.10, dialectal Arabic corpora have multiplied in recent years, with most of them being concerned with the varieties of Egyptian Arabic, Gulf Arabic, and the two macro-areas of Levantine and North African or Maghrebi Arabic. However, for the specific varieties of the last geographical group, only three corpora exist for the most investigated variety (Algerian), which are very different from each other as they are based on very divergent fields, i.e. one is extracted from newspapers and is specific to code-switching with French [START_REF] Cotterell | An algerian arabic-french code-switched corpus[END_REF], one is dedicated to YouTube comments (Abidi et al., 2017), and the other is based on an English lexicon automatically translated into Algerian and built for the purpose of Sentiment Analysis [START_REF] Guellil | Sentialg: Automated corpus annotation for algerian sentiment analysis[END_REF].

Tunisian Processing

Regarding Tunisian, Younes et al. (2018a) In fact, there is a good quantity of corpora including or focusing on Tunisian Neo-Arabic. However, there are not a lot of Tunisian corpora available for free. In tables 2.11 and 2.12, Tunisian corpora in Arabic script are outlined (those in Arabizi will be outlined separately, namely in table 2.13), highlighting whenever these are freely available.

As in the results shown in table 2.11 and 2.12, among the Tunisian-based freely available corpora there are the Tunisian Dialect Corpus Interlocutor (TuDiCoI) [START_REF] Graja | Lexical study of a spoken dialogue corpus in tunisian dialect[END_REF] and the Spoken Tunisian Arabic Corpus (STAC) [START_REF] Zribi | Spoken Tunsian Arabic corpus "STAC": transcription and annotation[END_REF], which is also morpho-syntactically annotated with a tag set based on Tunisian-Al-Khalil conventions (Zribi et al., 2013b), adopted for the purpose of the STAC. The STAC [START_REF] Zribi | Spoken Tunsian Arabic corpus "STAC": transcription and annotation[END_REF] is composed of 42,388 words collected from audio files downloaded from the web (as files from TV channels and radio stations) and then transcribed using the OTTA convention (Zribi et al., 2013a). OTTA is a Tunisian dedicated convention for orthographic transcription, which is oriented to Tunisian phonetics, unlike CODA Star [START_REF] Habash | Unified guidelines and resources for Arabic dialect orthography[END_REF], which is MSA-orthography-oriented, and was made to represent 28 Arab city dialects and to facilitate their processing. The STAC also contains 30 minutes taken from the TuDiCoI corpus [START_REF] Graja | Lexical study of a spoken dialogue corpus in tunisian dialect[END_REF], which is a domainspecific spoken-dialogue corpus that gathers a set of conversations between staff and clients recorded in railway stations. TuDiCoI consists of 21,682 words in client turns [START_REF] Graja | Discriminative framework for spoken tunisian dialect understanding[END_REF]. Another domain-specific corpus is the Tunisian Arabic Railway Interaction Corpus (TARIC) (Masmoudi et al., 2014b), which is also based on spoken conversations between staff and clients at Tunisian train stations. It contains 20 hours of TUN speech, manually transcribed into Arabic characters by three university students (Masmoudi et al., 2014b). TARIC is a task-oriented resource built for Automatic Speech Recognition (ASR). There are also two parallel corpora, one of which is the Parallel Arabic DIalect Corpus (PADIC) [START_REF] Meftouh | Machine translation experiments on padic: A parallel Arabic dialect corpus[END_REF][START_REF] Meftouh | Padic: extension and new experiments[END_REF] [START_REF] Meftouh | Machine translation experiments on padic: A parallel Arabic dialect corpus[END_REF]. Additionally, the MADAR parallel corpus [START_REF] Bouamor | The madar arabic dialect corpus and lexicon[END_REF] was initially a collection of translated sentences from English and French into Arabic dialects. In particular, as mentioned earlier, the source texts were selected from the Basic Traveling Expression Corpus (BTEC) [START_REF] Takezawa | Multilingual spoken language corpus development for communication research[END_REF]. With regard to the latter two instruments, these are therefore parallel corpora, acting as the raw material for several natural language processing tasks, such as cross-language applications including machine translation, bilingual lexicon extraction and multilingual information retrieval [START_REF] Meftouh | Machine translation experiments on padic: A parallel Arabic dialect corpus[END_REF]. However, it is also necessary to take into account that the texts The sentences are part of the MADAR CORPUS-6 that includes Beirut, Cairo, Doha, Rabat, and Tunis dialects. The corpus was created by a human annotator with the aid of a bootstrapping technique, and was manually validated [START_REF] Eryani | A spelling correction corpus for multiple arabic dialects[END_REF].

Corpus References Brief Description

OrienTel Transcription of real conversation into raw database for [START_REF] Iskra | OrienTel-Telephony Databases Across Northern Africa and the Middle East[END_REF] Gulf, Levantine, Egyptian and Maghrebi Arabic (Morocco, Algeria, Tunisia and part of Libya).

Part of the Orédule project Transcription of 10 h of TV and radio broadcasts in Tunisian, [START_REF] Belgacem | Construction d'un corpus robuste de différents dialectes arabes[END_REF] Algerian and other ADs, for system of speech-recognition. Oral Tunisian of Media Raw corpus of 5 h 20 min of radio and TV shows, [START_REF] Boujelbane | De l'arabe standard vers l'arabe dialectal: projection de corpus et ressources linguistiques en vue du traitement automatique de l'oral dans les médias tunisiens[END_REF] transcribed in CODA-TUN [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF].

The corpus has been built for language modelling aims.

Test corpus

For Tunisian POS-tagger building. 805 sentences containing [START_REF] Hamdi | Pos-tagging of Tunsian dialect using standard Arabic resources and tools[END_REF] 10,746 tokens, annotated using Lemma and POS tags.

DI Corpus

Raw corpus of Moroccan, Tunisian and three Algerian dialects [START_REF] Lachachi | Gmm-based maghreb dialect identification system[END_REF] collected from radio stations and TV show speeches, for automatic dialect identification.

Tunisian Arabizi Processing

As briefly outlined above, a number of studies on dialectal Arabic have been set up with the aim of solving the lack of a standard orthography through several different methods, such as rule-based, conventional orthography, language modelling and machine learning approaches. However, transliterations involving dialectal Arabic were mainly treated by adopting language models and orthographic conventions [START_REF] Younes | Romanized tunisian dialect transliteration using sequence labelling techniques[END_REF]. Nowadays, the most extensive Conventional Orthography for Dialectal Arabic is CODA Star, a unified set of guidelines for 28 Arab city dialects [START_REF] Habash | Unified guidelines and resources for Arabic dialect orthography[END_REF].

'As a general rule, CODA* uses MSA-like orthographic decisions (rules, exceptions and ad hoc choices), e.g., cliticizing single letter particles, using Shadda for phonological gemination, using Ta-Marbuta, Alif Maqsura, silent Alif in Waw-Alif of plurality, and spelling the definite article Al morphemically.'27

The main CODA features consist of:

1. Having a single orthography for each word;

2. Supporting Natural Language Processing for Dialectal Arabic;

3. Employing the Arabic script;

4. Unifying all Arabic dialects under a unique encoding framework;

5. Balancing morpho-phonological individual dialect representation and the exploitation of MSA-Dialectal Arabic similarities for unification purposes.

The first widespread convention for dialectal Arabic, named CODA, was proposed by (Habash et al., 2012a). Regarding Tunisian, the first CODA was adapted to Tunisian from the Egyptian CODA, resulting in the CODA-TUN (Habash et al., 2012a), by [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF], and has been used, for example, by [START_REF] Boujelbane | De l'arabe standard vers l'arabe dialectal: projection de corpus et ressources linguistiques en vue du traitement automatique de l'oral dans les médias tunisiens[END_REF], while the Egyptian CODA was instead used by Al-Badrashiny et al. (2014) to build 3ARRIB, i.e. a transducer trained at the character level to generate all possible transliterations for input words in Arabizi. Additionally, [START_REF] Bies | Transliteration of arabizi into arabic orthography: Developing a parallel annotated arabizi-arabic script sms/chat corpus[END_REF] used the Egyptian CODA to build a parallel corpus of Egyptian Arabizi-Egyptian Arabic of informal written conversation, such as for SMS and chats. Another work on social media Arabizi transliteration into Arabic script is that of [START_REF] Eskander | Foreign words and the automatic processing of Arabic social media text written in roman script[END_REF]. Regarding the CODA-TUN applied to Tunisian Arabizi (TA), the study carried out by [START_REF] Masmoudi | Arabic transliteration of romanized tunisian dialect text: A preliminary investigation[END_REF], who realized a TA-Arabic script conversion tool, which was implemented with a rule-based approach, should also be mentioned. With these aims in mind, the scholars collected a Tunisian corpus of texts written in Arabizi, automatically generating a set of its possible transliterations into CODA-TUN [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF] by applying transliteration rules. The best candidate was then manually selected by the annotator.

In recent years, several researchers, when dealing with the task of Arabic language transliteration, have adopted language models based on different approaches. These researchers included the work of [START_REF] Chalabi | Romanized arabic transliteration[END_REF], mentioned above, who implemented a hybrid rule-based and language models approach. Another example is the (2014). These three works are based on a character-to-character mapping model that aims to generate a range of alternative words that must then be selected through a linguistic model. In particular, the aim of [START_REF] Darwish | Arabizi detection and conversion to Arabic[END_REF] was to classify words as either Arabizi or English by using sequence labeling based on Conditional Random Fields (CRF). [START_REF] Eskander | Foreign words and the automatic processing of Arabic social media text written in roman script[END_REF] focused on foreign words and the automatic processing of Arabic social media text written in Roman script. [START_REF] Guellil | Arabic dialect identification with an unsupervised learning (based on a lexicon). application case: Algerian dialect[END_REF] presented an approach for social media dialectal Arabic identification based on supervised methods, using a pre-built bilingual lexicon, of 25,086 words, which was previously proposed in (Guellil and Faical, 2017;[START_REF] Azouaou | Alg/fr: A step by step construction of a lexicon between algerian dialect and french[END_REF]. The primary goal of this approach has been to identify words encoded in the Algerian Arabizi dialect.

Continuing the focus on Algerian Arabizi, Guellil and Azouaou (2017) proposed a Syntactic Analyser for the Algerian dialect. The authors focused on Algerian Arabizi and in order to build their parser, enriched a basic dictionary that contains translations between the Algerian dialect and French, with different phonological extensions. A different method is presented in Younes et al. (2018b), in which the authors present a sequence-to-sequence based approach for TA-Arabic characters transliteration in both directions [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF]. As regards Tunisian dialect transliteration in general, [START_REF] Younes | Romanized tunisian dialect transliteration using sequence labelling techniques[END_REF] noticed that very few studies have been developed, namely only [START_REF] Masmoudi | Arabic transliteration of romanized tunisian dialect text: A preliminary investigation[END_REF]; [START_REF] Younes | A hidden markov model for the automatic transliteration of romanized Tunsian dialect[END_REF]Younes et al. ( , 2018b)). Only a few studies have explored Deep Learning-based approaches for TD language transliteration, with one of those (Younes et al., 2018b) being a sequence-to-sequence approach and obtaining a word-level accuracy of 95.59%. [START_REF] Younes | Romanized tunisian dialect transliteration using sequence labelling techniques[END_REF] therefore proposed an exploration into a word-level approach based on modelling transliteration as a sequential labelling task implemented using different techniques (CRF, BLSTM and BLSTM-CRF). Finally, when evaluating the transliteration approaches at the sentence-level of their Latin-encoded Tunisian Corpus content, this showed that the BLSTM-CRF model outperformed the other models. As in the case of [START_REF] Younes | Romanized tunisian dialect transliteration using sequence labelling techniques[END_REF], most research on the automatic processing of Arabizi involves a preliminary phase of collecting a corpus, so as to train the models or test the various experiments. As we have seen in section 2.3, creating corpora from scratch is a very time and energy consuming practice; moreover, it is evident that experiments can only really be reproducible if carried out on the same data used by scholars, if same is made available. However, this is not always the case. In the following table (table 2.13), the Arabizi Tunisian corpora are therefore listed, including a note on whether they are available for free download (downloadable) or by contacting the author (contact).

Corpus References Brief Description

MultiTD corpus Gathers texts (32,848 words) from many sources: [START_REF] Bouchlaghem | Tunisian dialect wordnet creation and enrichment using web resources and other wordnets[END_REF] Manually annotated with polarity.

Table 2.13: Corpora including Tunisian Arabizi.

Chapter 3

Tunisian Arabish Corpus -TArC

After defining, in the previous chapters, the motivations that led to the building of a Tunisian Arabizi corpus from scratch and the methodology adopted by referring to the current state of the art, this chapter will move on to a description of the specific operations that have led to the realisation of this goal. The phases that will be described trace the path from its beginnings, that is to say, from data collection up to decisions relating to the selection and collection of metadata (see section 3.1). We will also describe the semi-automatic annotation phases of the corpus in the levels that it is composed of: transliteration in Arabic characters, tokenisation, part-of-speech tagging and lemmatisation. The phases and experiments that led to the identification of the best strategies for achieving the goal will be reviewed (see section 3.2 and 3.3). Finally, the Multi-Task Sequence Prediction Architecture that was built in order to produce the different annotation levels that make up TArC from the Arabizi text will be described (see section 3.4). This architecture was used to precisely achieve the main goal, but at the same time also constitutes a useful tool which makes it possible to extend the same work in the future. Through the same architecture it will be possible to extend TArC with further data that will be annotated automatically and which requires minimum participation in manual correction, and to adapt the same tool to the treatment of other Arabic dialects.

Data collection

Data & metadata selection

Considering what was discussed in the previous chapter (see section 2.2) about the nature of online conversation applied to the case of Arabizi, three main sources of DNW were identified in Tunisian Arabizi to collect the data we wanted to include in our corpus. The three sources of these written texts were conversations on social networks, on forums, and texts extracted from blogs. Regarding the latter genre, we have selected two Tunisian Blogs, Hatem Jouher 's blog and the TounsiaDigordia's blog, from whom we have requested written permission to use their texts. 1 The extraction of this data was possible through a web scraping tool, ParseHub, which permits precise extraction of selected textual data and metadata.2 Web scraping is a method of extracting data from several websites into a spreadsheet or database. The selection of data from blogs was actually limited by the identification of Tunisian blogs encoded in Arabizi. The only interference exercised on this data is that of having chosen one blog produced by a female author and one by a male author, in order to move as close as possible towards an equal representation of the two sexual genders. However, it was necessary to devise a more complex system for data extracted from social networks and forums, in order to automatically identify Tunisian texts encoded in Arabizi which were available on the Internet, in order to crawl (select and extract) the texts and metadata of the users.

In order to quickly identify Tunisian texts, or at least those produced in Tunisian territories, one option would be to use Twitter data, taking advantage of the possibility to extract text based on the location of users. However, Twitter's textual data was not suitable for our purposes. In fact, our goal was to collect texts of variable lengths, but possibly medium-to-long, so that they would be as contextualised as possible and rich in linguistic information. As such, the crawling process had to be organised differently.

The choices made resulted in a selection made of keywords that were unequivocally

Tunisian, but which were generic enough to not influence the data. In order to ensure that the work was as unbiased as possible, a corpus collection procedure was adopted, and was composed of the following: steps:

1. Thematic category identification.

2. Matching of categories with sets of semantically related TA keywords.

3. Texts detection based on keyword search.

Texts and metadata extraction.

The first step. In order to build a corpus which is as representative as possible of the Tunisian system, identifying wide thematic categories that could represent the most common topics of daily conversations on DNW was considered to be useful. In this regard, two instruments with a similar thematic organisation were employed:

⋆ 'A Frequency Dictionary of Arabic', and in particular its 'Thematic Vocabulary

List' (TVL) [START_REF] Buckwalter | A frequency dictionary of Arabic: Core vocabulary for learners[END_REF].

⋆ 'Loanword Typology Meaning List', which is a list of 1460 meanings (LTML) [START_REF] Haspelmath | The loanword typology meaning list: Electronic databases of 29 languages. A collaborative project coordinated by the Max Planck Institute for Evolutionary Anthropology[END_REF].

The TVL consists of 30 groups of frequent words, each one represented by a thematic word. The second consists of 23 groups of basic meanings sorted by representative word headings. Considering that the boundaries between some categories are very blurred, some categories have been merged, such as 'Body' and 'Health', (see table 3.1). 3 Some others have been eliminated, due to being irrelevant for the purposes of this research, including 'Colors', 'Opposites', 'Male names'. In the end, the fifteen macro-categories listed in table 3.1 were obtained.

The second step. By aiming to easily detect texts and their respective Source URLs, without introducing relevant query biases, it was decided to avoid the use of category names in the query, instead generating a range of keywords [START_REF] Schäfer | Web corpus construction[END_REF]. Therefore, each category was associated with a set of keywords in Arabizi belonging to the basic Tunisian vocabulary. A semantic category with three meanings was found to be enough to obtain a sufficient number of keywords and URLs for each category. For example, for the category 'family', the meanings: 'child', 'marriage', 'divorce' were associated with all their TA variants, resulting in an average of 10 keywords for each macro-category (see table 3.1). Concerning the keywords associated with the fifteen macro-categories in table 3.1, in order to avoid possible bias resulting from a specific spelling of an employed keyword and the associated words, native speakers were involved in the process. The latter were asked to write the keywords (in Arabizi) in all possible ways, which was presented to them written in Arabic script. A total of four native speakers of different sexual genders and age ranges (specifically, two males from the 25-35 and 50+ ranges and two females from the same ranges) were involved. 4Considering that these keywords did not serve to identify the exact texts to be extracted, but only served to lead us to Tunisian language forums, where we could then find people of different age groups, sexual genders, and backgrounds, these expedients were considered to be sufficient to avoid introducing bias during the text search phase.

The third step. The adopted Parsehub collection process consists of:

1. Manually insert the selected URL into the Parsehub client new-project section.

The system loads the page and shows, to the user, the possibility of selecting the data he/she wants to collect by clicking on an interactive area of the client, where the web page is shown.

2. Click on the first message. The system automatically identifies the structure of the page and highlights all the other elements which are similar to the one clicked on.

3. Click on the first highlighted message. The Parsehub system will select all the other similar elements until the end of the page and will contextually structure the selected data into a CSV/Excel or JSON format. The second possibility was chosen for this study.

4. Add relative select comments. These comprise information belonging to the selected data (i.e. the text's message), such as, for example, the message metadata. After the first relative selection, the system automatically repeats the same process on the other selected elements. 7izb,hizb,7ezb,hezb,dimo9ratia,dimo9ratiya,dimoqratia,dimokratia,hourriya,7ourriya,horriya,7orriya 15. Faith religion,god,Tawèīd din,eddin,dine,eddine,rabbi,rabb,tawhid,taou7id,taouhid,taw7id Considering that Parsehub permits the saving of the data selection project registering the different steps described above, it is not necessary to repeat these for each page in case where the same forum is used. Therefore, only the URL had to be changed to crawl a page from the same forum or blog.

Macro-Categories

The fourth step. After a manual check of the collected data to ensure that they were indeed Tunisian texts and relevant to the purposes of the research, approximately 25,000 words and their metadata were collected as the first part of the corpus. As can be seen in the following sections, the TArC data was augmented at a later time using the same techniques as those described here.

Regarding the collection of metadata, while this could be performed automatically using the tool for blogs (and some forums), for social networks it was necessary to proceed manually. The only metadata that was automatically collected by the web crawling system, and developed for purpose, was the date of publication of the text and the link to the profile of the user who was the author. In a second phase using that link, user data was manually collected where publicly available.

Concerning the date of publication, this was useful for assisting with the selection of data extracted from the web, in an attempt to make the corpus as balanced as possible in the diachronic representation. In fact, texts were chosen and collected from a range of approximately 10 years in order to observe their diachronic variation, where it was present. In particular, this selection was done with the purpose of observing to what extent the TA orthographic system has evolved toward a writing convention.

With regard to user metadata, the information posted by users was extracted, and the focus was on the three types of information generally used in ethnographic studies:

1. Gender: Male (M) and Female (F).

City of origin.

In a second stage, cities were traced back to the governorate to which they belonged, in order to simplify analyses of diatopic variation on TArC (as presented in chapter 4).

In fact, this user metadata was collected with the general intention of making diastratic variation analyses possible on the TArC data.

Corpus structure -annotation levels -first phase

Since Arabizi is a spontaneous orthography of Tunisian, it was considered to be important to adopt the CODA* guidelines [START_REF] Habash | Unified guidelines and resources for Arabic dialect orthography[END_REF] as a model for producing a unified encoding in Arabic script for each Arabizi token.5 'CODA* (pronounced CODA Star, as in, for any dialect) is a conventional orthography for dialectal Arabic. It is designed primarily for the purpose of developing computational models of Arabic dialects.'

This choice was primarily made to facilitate correspondence with existing tools and studies for Arabic processing. Since CODA* is a unified convention, the specific guidelines for Tunisian Arabic (CODA TUN) were also taken into account [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF]. These guidelines adhere to those of the CODA convention project. In fact, the negation mark that the CODA TUN proposes for maintaining the MSA rule, that is, the insertion of a space between the first mark of negation and the verb, was executed to make CODA TUN conform to the first CODA (Habash et al., 2012a). However, as explained by [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF], in Tunisian Arabic, this rule does not have a correspondence on the phonetic level, but should still be applied in order to preserve consistency across CODA guidelines. Indeed, in our transliteration we report what was produced in Arabizi following the CODA* rules, while in the lemmatisation we report the lemma of the verb. At the same time, on the tokenisation level, we segment the negative verb into its constituent parts: the negation prefix, the conjugated verb, and the second negation mark (if present).

In order to guarantee accurate transliteration, the first 6,000 tokens were manually annotated across all the annotation levels. Some annotation decisions were taken before this step, with regard to specifically Tunisian Arabic features:

1. Concerning foreign words, we transliterated Arabizi words into Arabic script, except for code-switching terms. Regarding the latter, at the Arabic encoding level (and the other levels, as can be sees later in this chapter), the token with the tag later used for its classification was replaced, i.e. 'foreign'.

2. With regard to typographical errors and typical problems related to informal writing habits on the Internet, such as repeated characters to simulate the prosodic features of the language, it was not possible to maintain all of these characteristics in the Arabic script level, according to CODA* conventions.

3. Regarding the phono-Lexical exceptions, the grapheme < >, [g], was only used in acclimatised loanword transliterations, such as the loan verb /rīg@l -yrīg@l/ 'to arrange', from French régler. Instead, the Hilalc phone [g], which can be found, for example, in the Arabizi word gamra, /gamr . a/, has been transliterated and lemmatised with the grapheme < >, [q]. 4. Concerning the glottal stop, as explained in CODA TUN, the real initial and final glottal stops have almost completely disappeared from Tunisian Arabic or have instead become a long vowel [START_REF] Zribi | A Conventional Orthography for Tunisian Arabic[END_REF](Zribi et al., , 2357(Zribi et al., , 2359)). Glottal stops still remain in some words that are treated as exceptions, such as the verb 'to ask a question' < >, /sPal/, also pronounced /shal/. Indeed, glottal stops are only transcribed when they are usually pronounced, and if not, glottal stops are not written at the beginning of the word or at the end, neither in the transliteration, nor in the lemmas.

Transliteration Model

In order to facilitate easier and faster corpus collection, a semi-automatic procedure based on sequential neural models was adopted (Dinarelli and Grobol, 2019a,b). To begin, we first used the first set of approximately 6,000 manually transliterated tokens as the training and testing data sets in a 10-fold cross-validation setting. Cross-validation is a statistical technique that can be used in the presence of a good numerosity in the observed sample. In particular, the so-called k-fold cross-validation consists of the subdivision of the total data set in k parts of equal numerosity, and, for every step, the k part of the data set comes to be the validation part, while the remaining part always constitutes the training set. As such, the model is trained for each of the k parts, therefore avoiding problems concerning the improper representation of the data which is typical of the data set subdivision in only two parts (that is training/validation sets) (Russel et al., 2009, p. 708). In other words, the observed sample is divided into groups of equal numerosity, one group is iteratively excluded at a time, and one tries to predict it with the non-excluded groups, in order to verify how good the prediction model used is.

As mentioned previously, the Arabizi tokens that were the result of code-switching were classified as foreign. However, the decision to include a level of classification of Arabizi tokens, in arabizi, foreign and emotag, came at a later date, with the aim of solving the following question. That is to say that, considering that the goal was to transliterate Tunisian Arabizi (not the result of code-switching) into Arabic characters, if the tokens resulting from code-switching were also transliterated, this would have generated noise for an automatic and probabilistic model. In fact, the correspondence between the phonological and orthographic planes of the two systems necessarily presents asymmetries. In this first phase of testing, however, not having yet identified an ideal treatment solution for these tokens, it was simply decided to remove them from the data. After removing the French tokens, the data was reduced to approximately 5,000 tokens. By combining the index of sentences, paragraphs, and tokens in the corpus, whole sentences can be reconstructed. Specifically, these 5,000 tokens corresponded to approximately 300 sentences, which is far too few to be used for neural model learning.6

In order to reduce the variety of data to be predicted, the Arabizi tokens were divided into characters and the Arabic tokens were divided into morphemes, and treated each token itself as a sequence. In doing so, the model was required to learn how to map Arabic characters into Arabic morphemes. The 10-fold cross validation with this setting gave a token-level accuracy of approximately 65%. This result was not satisfactory on an absolute scale, however it was encouraging when taking into account the small size of the sample. Using this model, approximately 700 additional tokens were automatically transliterated into Arabic morphemes. The manually corrected additional tokens were added to the training data of the neural model, and a new 10-fold cross-validation was performed. After this second stage, the results were approximately 70% on average. This procedure was repeated three times in total, so as to transliterate the first four blocks of TArC. The average accuracy on the fourth block was approximately 76%.

Once this level of accuracy was reached however, it became apparent that it would be very difficult to further improve the performance of this model, and a new strategy was evaluated that would allow us to:

1. Automate some of the operations performed manually, such as the classification of Arabic tokens, in order to solve the problem of noise caused by the presence of code-switching tokens.

2. Improve the performance of the transliteration task through the information contained in the other levels of analysis.

3. Share information across levels in order for same to improve one another.

From this insight came the idea of building a multi-tasking architecture, which will explored in the next section.

3.3 Corpus structure -annotation levels -second phase

String classification

As introduced in the previous paragraph, after the first experiments on the task of transliterating data into Arabic characters, the need to treat this data differently depending on its graphematic nature was imperative if also summarising it that way. It is true in fact that, as mentioned several times in previous chapters, Arabizi is encoded in Latin characters, and this facilitates switching to different systems that use the same encoding in Latin characters. However it is clear that the rules underlying the encoding of the system 'Tunisian Arabic' and that of the system 'French' for example, are divergent. The two in fact, despite sharing coding conventions, probably due to a strong linguistic contact continued over time, are governed by divergent historicallinguistic traditions. For example, looking at the TArC data, the first code-mixing element to be found is the French word 'mais ', pronounced [mE]. However, in Arabizi, the correct French spelling is maintained. As for Arabizi encoding words of Arabic origin, the Tunisian DNW system tends to reflect the phonetic realisation of Tunisian, rather than orthographic. If we had wanted to transliterate French code-mixing into Arabic characters as well, we would have had to choose between two options:

1. encoding French code-mixing being based on its pronunciation.

2. encoding French code-mixing being based on its spelling.

Both of these options were discarded for the following reasons: the first case would have increased the difficulties faced by the system at the level of disambiguation. For example, 'mais' would have to be encoded as < >, which is the encoding for both the first element of the circumfixed negations and the word designating 'water'. In the second case, it would be necessary to encode 'mais' as < > by generating a token that has no meaning in Arabic characters, which can be referred to as an artificial token. These artificial tokens would also contain generated noise for the neural system, for example, as in the phonetic realisation of French vowel clusters or silent consonants.

The system would have definitely had more difficulties in generalising the transliteration rules of Arabizi.

The initial solution was to remove the code-mixed tokens. However, this could not be considered to be a permanent solution. It was necessary to treat the two systems differently, and therefore first of all, it was also necessary to prefix all levels of treatment we wanted to provide for TArC, i.e. a level of classification of the text into code-mixing elements or elements with an Arabic-Tunisian etymological base. In short, we made a functional classification for the automatic treatment tasks, separating what should be transliterated from what should not be. In this way, the three macro-classes of foreign, arabizi and emotag were born. In addition, a class was needed also for all the paratextual elements which were typical of DNW. It was desirable to keep these for their potential usefulness in analyses, but they also created a good amount of noise for the neural system, due to largely being constituted of punctuation marks. Initial classification experiments were conducted using 'more controlled' data than that of TArC. 'More controlled' here refer to the use of data that contained only elements that belonged to one of the classes or a mixture of them. In fact, the texts of TArC contain texts of social networks, which are sometimes very informal, and at other times artistic or contain word-play, and therefore my be more difficult to handle from a computational point of view.

The token-level classification has been carried on through a RNN character-level model pre-trained on:

1. Hussem Ben Belgacem's French dictionary, consisting of 336.351 tokens.7

2. A Tunisian Arabish dictionary of 100.936 tokens, resulting from the merging of the following datasets:

(a) The Tunizi Sentiment Analysis Tunisian Arabic Dataset [START_REF] Fourati | Tunizi: a Tunsian arabizi sentiment analysis dataset[END_REF].8

(b) The TLD dataset of Arabish [START_REF] Younes | Constructing linguistic resources for the Tunsian dialect using textual user-generated contents on the social web[END_REF].

Therefore, the procedure employed to bootstrap the classification of the TArC data included performing model pre-training on an external corpus, by exploiting the above resources. This decision was made to speed up the process. In fact, as will be outlined below, an Arabizi corpus annotated with a classification level was initially built, instead of manually annotating a first block of TArC, as it was, for example, for the transliteration of the bootstrapping process. Moreover, in this way, it is possible to obtain a good quantity of pre-training samples without employing TArC data, such that classification was only performed once the model was already pre-trained.

The building of this pre-training corpus included the following steps:

1. Easily classifying all the tokens of Belgacem's French dictionary using the foreign tag.

2. Merging the Tunisian corpora into a unique corpus (TUN).

3. Using Belgacem's French dictionary to remove French words from TUN.

4. Building a small dictionary of emoticons (EMO) by using the emoji pythonlibrary.9 This was classified with the tag emotag.

5. Employing the EMO corpus to extract, from TUN, the smileys and emoticons contained in same, and obtaining a clean Tunisian Arabizi corpus that has been classified, at the token level, with the arabizi tag.

6. Merging and shuffling all the three classified corpora together, and obtaining the pre-training corpus.

The model pre-trained on the above data reached 97% accuracy. At this point, it became possible to start an iterative procedure for the TArC text classification. The custom of dividing TArC into blocks of approximately 6,000 tokens was maintained.

After classifying each block, it was manually checked and added to the training data in order to improve the performances thereof. However, the accuracy on the last TArC block (the seventh) remained at 97%. As will be seen in section 2.4.4, when it was decided to perform all the TArC annotation levels through an architecture, the classification task was repeated, together with the other tasks. As shown in table 3.4, the architecture accuracy of the classification task slightly improved in the last step (97.63%) in comparison to the mono-task model described here. The same table also summarizes the results obtained at each step during the TArC classification.

Tokenisation

Considering the high index of synthesis that characterises Arabic dialects, which is albeit less than that of Standard Arabic, it was considered fundamental to have, as a third level of TArC annotation, a tokenisation level.10 In Tunisian Arabic, each string consists of one root that has a basic meaning. Affixes are added to make the stem word incorporate the subject, direct and indirect objects, numbers, gender, definiteness, etc., as shown in the following example (20). The tokenisation at string level deals with reducing each string to its morphemic clitic components, concatenated by the symbol +.

This also reduces data sparsity and decreases the number of out-of-vocabulary (OOV) words, supporting the other annotation levels. Only those tokens classified as arabizi, and thus transliterated, have been tokenised, following the D3_BWFORM configuration scheme of MADAMIRA tools, where basically all clitics are tokenised, including the article [START_REF] Pasha | Madamira: A fast, comprehensive tool for morphological analysis and disambiguation of arabic[END_REF].11

(20) Arabizi: dbrthelik

/d@bbart-hā-l@-k/ < > < > (transliteration in CODA*) (tokenisation)
'I turned things around for you' (lit.:I found her (the solution) for you).

This scheme was chosen because the BWFORM method is the only one, among those provided by MADAMIRA, to support dialectal Arabic input text (which is specifically trained on Egyptian Arabic). Regarding sentence boundaries, a boundary token, i.e.

'<eos>' (meaning 'end of sentence'), was automatically added after each final punctuation mark, and same were then checked manually. Manual correction was quite important in this case because, especially in the case of texts coming from social networks, it is possible to find very long texts without any punctuation marks. Consequently, in addition to the automatically inserted end-of-sentence marks, at least another 30% of marks were added manually.

Part-of-Speech

Part-of-Speech tagging is the morphosyntatic annotation of strings. It has been carried

out mainly at the morphological level, and in the case of adverbial phrases or interjections being composed of multiple words, also at the functional level (Bender and Lascarides, 2019, 57-58).12 As can be seen below, it was necessary to incorporate a variation to the chosen POS-tagging system in order to be able to include this additional layer of information. Regarding the morphological POS-tagging level, this describes the morphological nature of each element of the string, while the other one describes the grammatical function of the whole string. The POS annotation style follows the guidelines of the Penn Arabic Treebank (PATB) [START_REF] Maamouri | The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus[END_REF]. The latter uses the Buckwalter tag set that presents a high degree of granularity. It is precisely because of this granularity and the possibility of tagging each morpheme that Buckwalter's annotation scheme was chosen. Buckwalter's annotation scheme includes the following tags (3.2). At the beginning of the project, the intention was to use Universal Dependencies (UD),13 specifically the New York University Abu Dhabi Universal Dependency Arabic Treebank (NUDAR) developed by [START_REF] Taji | Universal dependencies for arabic[END_REF]. However, NUDAR is a tagset for Standard Arabic and, in particular, is the conversion of the Penn Arabic Treebank into the syntactic representation system of the UDs through an intermediate dependency representation. For these reasons, a more adequate solution for Tunisian Arabic tagging was evaluated, i.e. the direct use of the Penn Arabic Treebank tagset. In fact, the operation of POS-tagging of dialectal Arabic, and moreover of its informal social networking variety, required a meticulous analysis of each morpheme. Furthermore, it is easier to automatically convert a fine-grained tagset into a more simple one. It seemed to be better to ensure a fine-grained tagging in a first level of analysis of the TArC morpho-syntax, without at all excluding the possibility of generating a conversion of the adopted tag set to the UDs at a later stage. In fact, since the purpose of UDs is to facilitate the creation of treebanks in different languages that are consistent in their syntactic representation, while allowing for the extension of relationships to accommodate language-specific constructs, this would be the ideal solution in both NLP and in linguistics for comparative purposes with other languages. Regarding the functional POS tagging level, it was deemed useful to add information such as the adverbial function of phrases, such as the one shown in the following example ( 21): As can be seen in example 21, morpho-syntactic annotations have been enclosed within square brackets, outside of which we have provided the functional annotation, since the prepositional phrase performs the syntactic function of an adverb. This variation in the annotation system has been evaluated as enriching the corpus in terms of information, which does not affect the level of compatibility with other corpora annotated according to the same scheme. In fact, in the case where the users of TArC do not want to take into account the functional annotation, they can simply choose to exclude (also at the level of automatic processing) the elements from the square brackets and limit their query to the morphological annotations. Furthermore, since Buckwalter's tag set was created for the annotation of Standard Arabic, certain tags such as the <CASE>, the <CONNEC_PART> or the <LATIN>, <FOREIGN>, <PARTIAL> and <DIALECT> tags (in table 3.2) are never used in the annotation of our corpus, as they are not tags which are compatible with the morphological structure of Tunisian Arabic.

In general, our approach to POS-tagging has been to follow Buckwalter's tag set, trying to remain as faithful to it as possible, by using the Penn Arabic Treebank's annotation guidelines.14 At the same time, we sought a compromise appropriate to the representation of the morpho-syntax of Tunisian Arabic and a strong consistency in the use of tags in order to ensure a good result of the automatic processes. In order to simplify the work, in the initial phase, the Tunisian MADAR corpus data [START_REF] Bouamor | A Multidialectal Parallel Corpus of Arabic[END_REF] was also automatically POS-tagged using the MADAMIRA software [START_REF] Pasha | Madamira: A fast, comprehensive tool for morphological analysis and disambiguation of arabic[END_REF]. The results, as MADAMIRA is trained only for Egyptian among the Arabic dialectal varieties, were not satisfactory for Tunisian Arabic. However, despite this, this step was very useful for starting to familiarise ourselves with a POS-tagged text and to observe the various tag combinations on a Tunisian Arabic text. Finally, the scheme we used for out tag set was the following (scheme 3.3), in which the ∃ symbol means that the tag can receive additional morphological features, while the symbol Ø means the opposite.

PV_PASS

-PVSUFF_SUBJ (1S/2S/2P/3MS/3FS/3P) Ø

Lemmatisation

As will be discussed later in this chapter, lemmatisation is a substantially important annotation layer. Present in our projects since the beginning, this was the last level we decided to produce for practical reasons. In fact, despite the usefulness of lemmatisation, transliteration in CODA*, tokenisation and POS tagging are indispensable layers for one another and are necessary for TArC completeness and for the analysis we present in Chapter 4. However, even lemmatisation represents a tool of fundamental importance both at the level of linguistic analysis and data processing (Zalmout and Habash, 2019b). The procedure used to generate this layer was the same as that implemented for all the others, i.e., the incremental iterative procedure based on automatic lemma annotation generation (via the Multi-Task Sequence Prediction Architecture 3.4) and manual correction of data, which was then used to augment the system training data. Once again, data from the MADAR corpus was exploited, for which we semi-automatically generated the lemmatisation level, using a first block of the manually lemmatised TArC as training data. The TArC lemmas are encoded in Arabic characters in the orthography also used for the Arabizi transliteration level, i.e. CODA*. This decision was made on the basis of factors of utility in linguistic research on TArC data. In fact, the CODA convention allows for normalisation of the text and a unique coding of the lemma, unlike the Arabizi encoding. 15 In order to ensure a good consistency and rigor, both linguistically and computationally, the following methodological choices were adopted when annotating the data of this level.

1. Considering that the inclusion of a root level is foreseen in order to simplify the matching of same with other Tunisian corpora or glossaries, it was not considered necessary to refer adjectives or deverbal nouns to the etymological verbal form. 15 We even considered an encoding in Latin characters, according to the dialectological tradition. However, we assumed that this would create difficulties in writing queries for linguistic analysis, based on the choice of specific graphemes, which are explained in an additional user guide. Additionally, we did not want to rule out the possibility of adding this encoding in the future for the level of annotation of the roots of each lemma.

In this way, we did not have to make a selection between those adjectives (or present and past participles) that are now so crystallised in the language in their nominal or adjectival form that they are perceived as nouns or adjectives rather than deverbal elements.

2. Adjectives with nisba are present at the lemma level in their adjectival, masculine singular version. 163. Collectives nouns that quantify one unit such as /žr . āna/, 'one frog' or /burdgāna/ 'one orange', are always brought back to the masculine singular i.e. /žr . ān/ and /burdgān/ .

4. In the case of elements of French origin, but which have also entered the Tunisian lexicon through transliteration, as with the adapted loanword /r@stūrānāt/, 'restaurants', as mentioned in chapter 1, (section 1.3.2), these have been reported in Arabic characters (, ,) and also in TArC. However, for their lemma, the French etymological lemma is reported, i.e. 'restaurant'.17 or SYM, show exactly these entries at the lemmatisation level.

6. Interjections, such as /nšāl ˙l ˙a/, 'God willing', are reported exactly as in the transliteration level in CODA* ( ) and have not been etymologically reduced to a single element, having a unitary semantic value.

7. In the case of articulated nouns (DET+NOUN ) or items preceded by prepositions, the noun lemma has been reported. In the case of articulated prepositions, the preposition has been reported as lemma. The article constitutes a lemma only when it appears as an isolated token. In the case of morpho-syntactically complex relative items (i.e. PREP+REL_PRON ), the relative has been reported as a lemma.

As will be explored in even greater depth in section 3.4, computationally speaking, the belief is that this level of annotation could make improvements to the CODA* transliteration task. For this reason, following the order of the tasks performed by the multi-task system, it was decided to place the module dedicated to the lemma between the classification module and the CODA* transliteration module.

Multi-Task Sequence Prediction Architecture

As mentioned above, considering the correlation between the different annotation levels expected for the TArC corpus, it is logical to produce these levels using a multitasking architecture rather than through models dedicated to single tasks. Indeed, a neural architecture factors in some parameters for information that can be shared between tasks, and then uses different modules for each task, which are learned interdependently. One idea was for these shared parameters to aid in disambiguation when annotating other layers. According to our insights, the first level of classification would have operated the selection of tokens to be encoded in CODA* and to then be tokenised, POS-tagged and lemmatised as learning tasks organised in a chain in a neural network, benefiting from each other at the disambiguation level, thanks to the sharing of parameters along the network. This insight led to the idea of a multi-task neural network, where different learning tasks are organised in cascades (Gugliotta et al., 2020;Gugliotta and Dinarelli, 2020a). An input consisting of text in Arabizi undergoes the following five tasks:

1. Classification

CODAfication

Tokenisation

POS-tagging

Lemmatisation

Regarding the order presented in the above list, this does not represent the final multi-task system order for the tasks organised in a cascade, but instead indicates the order in which the various layers were produced.18

As shown in figure 3.1, the Arabizi input is converted into context-aware hidden representations by the encoder based on recurrent layers (see section 2.4.3).

Steps 2, 3, 4 and 5 consist of interdependent decoders, which not only receive, as an input, the output of each of the previous steps (in the form of a hidden state), but also receive the Arabizi input processed by the encoder (i.e. the encoder output).

Regarding the figure, x represents the Arabizi input, which passes through the Encoder and is transformed into the encoder hidden state (h E ) and sent to different modules dedicated to the individual tasks:

1. Decoder cl for Classification 2. Decoder ar for CODAfication, 3. Decoder tk for tokenisation, 4. Decoder pos for POS-tagging.

5.

Decoder lm for Lemmatisation.

Here, once again, the order does not represent the final order of the annotation levels produced in a cascade by the multi-task system. In fact, if, from the above list, the last module (Decoder lm ) is included, it is possible to obtain the structure thanks to which we obtained the annotation of the lemmas. However, it has been found that the lemmatisation task may be more helpful for the CODAfication task if placed before it, so in the end, the modules of the multi-tasks architecture are presented in the following order:19

1. Decoder cl for Classification 2. Decoder lm for Lemmatisation. Each decoder gives its predicted output (ô 1 , ô2 , ô3 , ô4 and ô5 ) and a hidden state (h 1 , h 2 , h 3 , h 4 and h 5 ), while h E is the encoder's hidden state. Moreover, each decoder has a number of different attention mechanisms (see section 2.4.3), including one for attending to encoder's information, and one for each previous decoder's hidden state.

The predicted output is used to learn the single task by computing a single loss (i.e.

L 1 (o 1 , ô1 ), which refers to the comparison between the predicted output of the Classification, in this case, ô1 with the expected output o 1 ). Each loss (L 1 , L 2 , L 3 , L As described in the previous section, in order to obtain the CODAfication annotation level, a dedicated mono-task sequence-to-sequence model, the one of Dinarelli and Grobol (2019b), was used. Using this model, annotation was performed up to the fourth block of TArC, obtaining an accuracy of approximately 76%. We identify this procedure as the first phase (described in section 3.2.1). The second phase concerns

L 1 (o 1 , ô1 ) L 2 (o 2 , ô2 ) L 3 (o 3 , ô3 ) L 4 (o 4 , ô4 ) L 5 (o 5 , ô5 ) + L h E h E h E h E h E h 1 h 1 h 1 h 1 h 2 h 2 h 3 h 2 h 3 h 4
TArC annotation in all its levels. In addition, in the second phase, which was developed with the multi-task system, we repeated the iterative semi-automatic annotation procedure of the first phase for several levels (classification, tokenisation, POS-tagging and lemmatisation). A summary table of the steps is table 3.4.

In order to train the model to annotate the first block of TArC, Tunisian Arabicencoded texts belonging to the MADAR corpus were employed [START_REF] Bouamor | A Multidialectal Parallel Corpus of Arabic[END_REF], as previously introduced. The MADAR texts used amounted to approximately 12,000 tokens, i.e. 2,000 sentences, and were manually checked in order to be consistent, at the orthography level, with the CODA* conventions, not being encoded in CODA*.20

The same data, as only rough texts, were also annotated on the tokenisation and POStagging levels, by exploiting the MADAMIRA tools, and manually performing checking.

Regarding the classification level, each token of this data is classified as arabizi, in consideration of the efforts done by MADAMIRA builders in order to avoid any code-switching [START_REF] Bouamor | A Multidialectal Parallel Corpus of Arabic[END_REF]. The MADAR data has been used to train a first model which is useful for starting the iterative procedure of TArC annotation with all its levels (step 0 in the table 3.4). However, it was also helpful to merge MADAR training data with TArC training data (after manually performing correction) for annotation of the next block. Indeed, this merging was possible until TArC data was already encoded in Arabic-script (until the fourth block). Different solutions were tried for the annotation of the last three blocks:

1. Using only TArC data as training data, since MADAR is not provided with an Arabizi encoding level (Step 4 and Step 5 in table 3.4);

2. Using only TArC data as training data, together with ad hoc parameter initialisation, in order to exploit the model trained at step 0 (on the MADAR data) (smart init steps in table 3.4);

3. Provide MADAR with an Arabizi encoding level and merge same with the TArC data (steps found under the header M ADAR Arabizi +TArC in table 3.4).

In the following table (3.4), it is possible to observe the results of the different strategies tried. The tasks are indicated as Class for classification, CODA* for the CODA* encoding, Token for tokenisation, and PoS for POS-tagging. In the same Regarding table 3.4, Step0 represents the bootstrapping phase of the multi-task system, using only MADAR data. Moreover, Step0 complete uses only the MADAR data, but also includes the semi-automatically produced MADAR Arabizi annotation. The next steps (1, 2, 3) also employ the TArC blocks, respectively one block (the first of TArC), two blocks (the first and the second) and three blocks (from the first to the third). As mentioned above, these TArC blocks were already provided with a CODA* encoding level thanks to the preceding models, and were manually checked. For this reason, it is possible to concatenate MADAR and TArC until the third step. In the next steps (Step4 and Step5 in the table), the first solution mentioned above was adopted and employed only when the TArC data observed a substantial drop in the tokenisation and POS-tagging accuracies. This is due to the following reasons:

1. Arabizi (less consistent than CODA*) has been used as the input;

2. The presence of less training data;

3. An additional annotation level to be predicted by the system (CODA*/Decoder 1 in figure 3.1).

Regarding the drop between Steps4 and Step5, this is due to the presence of different text genres in block 5. In fact, Step4 is mainly constituted of forum texts, while

Step5 texts came from blogs, which are quite different from the previous ones (see section 2.2 for theoretical observation about the differences among textual genres and chapter 4.4 for the analyses concerning this topic). As mentioned in Gugliotta et al.

(forthcoming), another relevant observation that emerges from the performances in these two steps is that the most ambiguous annotation level, which uses Arabizi as an input, appears to be the Arabic-script level.

Therefore, we tried the second solution, represented by the experiments, which results are reported in the entries Steps4 smart-init and Steps5 smart-init . This kind of initialisation, exploiting the model trained at Step0 on MADAR texts only, consists of making substantial improvements on each task (excluding the classification one, which has already been solved). Thanks to this experiment, it is possible to verify the usefulness of MADAR data for the multi-task system. In fact, the MADAR data, although generated through procedures that do not exactly coincide with dialectological methodology, represent a useful source for texts that are consistent in their morphosyntactical structure.21

In light of the foregoing, a third method was also tried, with the intention of further improving the results of the three tasks thanks to a fuller exploitation of the MADAR data. Therefore, MADAR data has also been annotated through a semi-automatic procedure at the Arabizi script level. The latter simply consists of inverting the process employed to produce the CODA annotation level from an Arabizi input, and a subsequent manual correction of the Arabizi output obtained from the Arabic-script input.

Once the MADAR was also annotated at the level of Arabizi, in order to compare the

Step0 results with a new model trained only on MADAR (but including the Arabizi level), we repeated the Step0 annotation procedure. The result of this is defined as

Step0 complete in table 3.4. Comparing Step4 under the header MADAR Arabizi +TArC and Step4 produced only with TArC data and with the smart-init strategy, it is possible to observe an improvement in the accuracies of all the tasks. Using the MADAR provided with the Arabizi level is definitely the best solution in terms of our aims.

Therefore, TArC block annotation was continued, with the last step (Step6 ) being used to annotate the seventh and last block. This step shows further improvements on all levels. Related to the issue mentioned above and in Gugliotta et al. (forthcoming), namely, the fact that transliteration into CODA* is the most difficult task for the system, additional experiments were performed on individual tasks. These are presented in the table as Step6 Arabic , Step6 Token , Step6 POS . These mono-task experiments show us that all the tasks take advantage of the multi-task organisation, except for the tokenisation (89.54%). However, due to the fact that the latter is produced from an input consisting of the correct CODAfication level, it is not surprising that the mono-task result is so good in comparison to the multi-task-Step6. In fact, this result confirms that:

1. The performance of the multi-task system on the tokenisation task is largely affected by errors in the CODAfication task.

2. The multi-task system helps improve both the CODAfication and the POStagging levels.

3. When given a correct CODAfication level, the tokenisation task is a relatively easy task to perform.

4. The CODAfication is the hardest-to-produce level for the multi-task system.

5. By improving the CODAfication task performances, a lot of the other tasks performances could also be improved.

The fact that the transliteration in CODA* represents the most difficult task for the multi-task system is not surprising at all, especially since it is based only on the selection made by the classification level and the Arabizi input. The first of these only selecting a token suitable for transliteration and the second being an informal and non-standardised encoding results in a noisy input for the system.

In order to definitely prove this consideration, the last experiment (Final Step6 CODA * input has also been reported in table 3.4), which presents the highest accuracy values on the different tasks which are jointly performed. For this experiment, the system received the Tunisian texts written in CODA* as an input, instead of their Arabizi encoding.

In order to achieve better multi-task system results on the CODAfication task, one last strategy was taken into consideration, which consisted of adding a lemmatisation level. We were already planning to add this layer, but due to time constraints we had considered finalising this issue at a later date.

TArC's data description

In the preceding chapters and sections, we have tried to express the principles that moved us to pursue the project of creating the TArC corpus. This section summarises the main features of the work in its final state.

As mentioned earlier, TArC is the result of multidisciplinary work with a hybrid approach, which seeks to make the most of the different forces that come together at the intersection of the disciplinary fields in which it arises. TArC was conceived with the intention of trying to respond to a need, i.e. that of making up for the lack of resources for an under-researched language. The entire project was inspired by the centuries-old tradition of experts, scholars and experts in Semitic languages, and in particular in Arabic dialectology. The aim was to get as close as possible to the ranks of the great linguistic corpora, which were made so remarkable with respect of the linguistic datum and the methodological rigor. At the same time, this project also tries to embrace the most advanced techniques of deep learning and its revolutionary power, which today is the engine behind the greatest experiences with artificial intelligence when same is applied to linguistics. It is certain that these systems require an enormous amount of data, which implies an equal amount of effort in terms of quality control. However, by automating the building process, some problems related to the influence of the data during the collection phase can be easily bypassed (e.g. see the observer paradox of [START_REF] Labov | Sociolinguistic Patterns[END_REF], which is a much discussed issue in dialectology [START_REF] Boberg | The handbook of dialectology[END_REF]).

The data collected in TArC, along with various metadata, provides a snapshot of Tunisian Arabic writing in Arabizi over the last ten years. In the following table (table 3.5), some quantitative data is reported with respect to the texts collected, and same is organised according to textual genres (forums, social and blogs) and the musical genre of rap. The quantities of tokens according to their classification in arabizi, foreign and emotag are also reported. research developed through TArC's data. In order to protect the privacy of our informants, only public information was collected. Sensitive data (such as proper names, phone numbers or links to personal pages) was also anonymised by covering same with a dedicated token. This token had to be encoded in the Arabic script when hiding a proper noun in the CODAfied annotation level. For this reason, a Tunisian word, namely <m5abbi>, was directly employed for the level in Arabizi, while < > is used for the levels in CODA*. This word means 'hidden' and is accompanied by a numerical code that always identifies the same token that has been covered.

Sentences Words

Statistics about the metadata quantity is given in the following tables. Tables 3.6, 3.7, 3.8 and 3.9 are about the different genres collected in TArC and made available through different files as mentioned in section 2. 3.2. 22 The total number of tokens for each text genre is also given in the same tables. The tables show the percentages of information collected for each variable expected from our metadata collection system (i.e. four age ranges: under 25 years, between 25 and 35 years, between 35 and 50 years, and over fifties).

The total percentage is represented by the number of tokens for which information was collected. For the publication date information, the total number of tokens making up the genre collection always corresponds to the total number of tokens reported at the top of the table (i.e. for the Social Networks table 3.6, 16,056 is the total number of Social Network tokens and is also the total number of tokens for which the publication date has been collected). It is not the same in the case of the users' metadata, considering that collecting their personal information was not always possible, depending of their profiles and the information which is shared publicly. As such, one an example, in Social Network data, users' provenience is a piece of information collected for 10,980 tokens, age range information for 11,313 tokens and gender information for 12,929 tokens. Percentages are given on the basis of these tokens numbers reported at the top of the table under the voice tot. With regard to the information on governorates to which users belong, sometimes users are from cities which are not in Tunisia, and in such cases, the information has been registered in TArC, among the governorate metadata.

However, these non-Tunisian cities are not taken into consideration in these metadata tables (Social Networks: 3.6, Forums: 3.7, Blogs: 3.8 and Rap lyrics: 3.9), but they are included in table 3.12, which is a specific table of the registered locations. Finally, as an example, tot: 10,237 in table 3.6 shows the total number of tokens in which a

Tunisian governorate has been collected as the user provenience. 23 If the tot voice does not appear at the top of the table, as it is not for the voice, i.e. Years in table 3.6 (or all the voices in table 3.8), this means that the tot voice coincides with the tokens number reported at the top of the table, as in 16,056 tokens in table 3.6.

Therefore, table 3.6 reports information about users whose texts are collected in TArC. The table also shows the percentage of female and male users, the percentage of each age range occurrence, and the percentage of tokens for each governorate.

script, thus making singer metadata mostly irrelevant. 24

Finally, we have also reported some global information of TArC, such as the token percentages for each year, in table 3.10, and the global percentage of tokens which have been registered as age range and the gender information, in table 3.11. The last table (3.12) shows the percentage of tokens for which a Tunisian governorate has been recorded. The number of tokens for which a Tunisian governorate has been registered is 20,595. In addition, in the same table, some of the most frequent foreign origins are also reported, which were recorded with the state name and which are a part of the number considered to be the total (i.e. 21,516 tokens). 24 Furthermore, by collecting data in both Arabizi and Arabic characters, we could create a complete Language Model for written Tunisian Arabic, following a suggestion by Prof. Houda Bouamor whom we thank.

In the simplest of terms, it is the comparison of the frequencies observed in certain categories with the frequencies we might expect to get in those categories by chance (Field, 2009, 688). 25 It is then the standardisation of the deviation for each observation.

For each analysis carried out, the significance of same with the p-value set at a threshold of 0.05 was evaluated, as is quite common in linguistic analyses. 26 The following section details the amount of data and metadata collected and presents the TArC-based linguistic analyses. Emphasis was placed on ensuring the reproducibility of each process carried out, both in the construction of the corpus and in the analyses performed on it. This was possible thanks to the building process adopted. Indeed, we made use of every available tool, including the scripts created for each analysis on the reference web sites. 27 Chapter 4

Analyses developed on the TArC

After the TArC data description given in section 3.5, together with some information about the data and metadata quantity, this chapter will describe the analyses carried out on the corpus. Before entering into the outline of the analyses, some brief details will be given about the query tools employed in section 4.1. The analyses realised are aimed at outlining the nature of the corpus itself, i.e. the description of the users, whose texts have been collected in TArC, and at investigating the linguistic reality of Tunisian Arabizi. Regarding the linguistic reality of Tunisian Arabizi, the analyses will move along three paths:

1. Quasi-orality traits (section 4.2); 2. Spontaneous settling trends (section 4.3); 3. Continuum of formality degree (section 4.4).

Finally, in section 4.5, we will discuss the possible conclusions that can be brought about in light of the phenomena highlighted throughout the analyses.

Query tools employed

The tools used to perform the analyses are simple query systems, made up of a set of Python functions, created specifically to observe the TArC data. In order to make these analyses reproducible, the query scripts created for each analysis have been made available at the website where it is also possible to download the corpus data, along with instructions for their use.1 Additionally, in order to observe the results obtained, without using any computational tools, the output of each script is available in the appendix of this manuscript. In general, query tools are systems created in order to quickly identify and select the data that we want to observe.

As an example, the first type of analyses, in section 4.2, is necessary to observe Prepositional Phrases (PPs), which are selected according to their internal composition.

The structure of PPs has been identified by iterating on the morpho-syntactic annotations, which TArC has been provided with (see section 3). The script created for the quasi-orality analysis returns the frequency's percentage of the various orthographic realisations examined, following the instructions provided through the query functions.

Moreover, depending on the level of depth of the data analysed, additional information can be requested from the system, such as the number of occurrences of prepositions involved in the various types of orthographic realisations. It is also possible to observe the textual data that makes up the different PPs, which are organised according to two types of structures, as shown in the examples 22 and 23. The representation of the two structures of a TArC PP, of the type [prep+n], used by the system in order to carry out the analyses, is provided below. The first of these (ex. 22) compares the token encoding in Arabizi (arabizi ) with that in Arabic characters (word ). The second (ex. 23) functions for the identification of the internal composition of the PP. This includes the alignment of the word in Arabic characters (word ) and its reduction into the morphemes that compose it (tokenisation). Both structures associate the morpho-syntactic annotation produced on TArC data (POS ) with the textual elements. The structure of the output files is a similar structure to that of XML files (eXtensible Markup Language) for data ordering reasons.

3 'Put the flour and salt in and pour on them 50 gr. of melted butter and mix'. My own translation.

PP(1). <Mixed PP: [lel famille]> <POS: PREP+DET_foreign>

<TArC idx: 1800-1801> <Within sentence: ena houni mochkelti win todkol mra jdida lel famille nwali nakraha nakraha griba w blech sbab !!>4 <Genre: social> <Users' metadata:> <origin:/> <age:/> <gender:F>

Quasi-orality traits

As seen in chapter 1, Arabizi belongs to Computer Mediated Communication (CMC)

and in particular to Digital Networked Writing (DNW) contexts. Indeed, it is a writing system which was born for digital communication. Its hybrid peculiarity as a quasi-oral system has already been discussed, including the characteristics of encoding an underresourced language, i.e. Tunisian, which is mainly oral, but which uses the Arabic script when written down. As a result, Arabizi seems to be partially influenced by the writing practice of the Arabic script, and partially intended to better represent some oral peculiarities of Tunisian Neo-Arabic, which are not properly encoded by Arabic graphemes. In fact, in section 1.3 of chapter 1, it has already been stated that no system, whether based on Arabic or Latin characters, was created to encode Tunisian phonology. However, the Arabizi system seems to mirror Tunisian Neo-Arabic phonetics, while the Arabic-script-based system seems to be more oriented to reflecting the morpho-phonological structure of Arabic. Therefore, subsection 1.3.3 provides an observation that one of the properties of Tunisian which is 'better encoded' with Arabizi than in the Arabic script-based system is the treatment of short vowels, including metathesis and diphthongs, in addition to some consonant realisation, such as the loan phonemes /p/ and /v/ or the /q ∼ g/ allophones (see section 4.3.3 for the analyses concerning these points).5 On the other hand, among the Arabizi writing features influenced by the Arabic script, hyper-correction phenomena such as the epenthetic vowel use at the beginning of #CC clusters need to be contemplated. As already observed, this phenomenon is typical of the Arabic-script orthography, where it is codified through the Palif grapheme, and same aims to transform the Tunisian #CC cluster into #VCC, which is easier to realise. This phenomenon has already been encountered in chapter 1. 3.3, and this feature is also present in Arabizi (as in the example given below, ex. (15), for example, in the Arabizi word etkoun, /tkūn/), in which an epenthetic vowel can be observed at the beginning of the same cluster.

However, instances of this are found much less often in Arabizi in comparison with its occurrence in Tunisian encoded in Arabic script.6

(15) t'es dans une ces regions donc yelzmek etkoun men mdina men ces regions alors que t'es mehdwi, /t'es dans une ces regions donc y@lzm@k tkūn m@n mdīna m@n ces regions alors que t'es m@hdwi/, 'You are in one of these regions so you should come from a city of these regions, that should make you a Mehdia citizen'

Regarding Tunisian encoding in Arabic-script systems mirroring Arabic morphology, examples of this will be given in sentences ( 11) and ( 14).

However, considering Arabizi as a not standardised system, it is possible to suppose that it may allows its users to be more independent from strong writing traditions, such as those concerning the Arabic script. This is the cornerstone of our first hypothesis about Arabizi. What needs to be investigated precisely is whether this freedom of encoding is constrained by the fact that Arabizi belongs to a written context, and normally, before the invasion of Facebook, writing in Tunisian corresponded to writing in Arabic characters and according to some standardised rules of 'correct encoding' (see hypercorrection). The alternative instead is that Arabizi, by creating more freedom with which to render the characteristics of Tunisian, which do not coincide with those of Arabic, allows the user to get rid of some rules normally imposed by Arabic orthography and everything encompassed by the idea of 'correct encoding' in Arabic. This possibility includes Arabizi being influenced by French orthography, being encoded in Latin characters, and the ability to facilitates the use of French vocabulary in codemixing. Moreover, in a completely different context, but which still involves the Arabic language, i.e. that of bilingual contexts and texts written in a system that is not the one usually intended for that language, it has been verified that the mismatch between the norms of the two languages can generate coding uncertainties that may lead to the spread of linguistic interference.7

However, in the case of Arabizi, different phenomena are involved. It is true that the Latin alphabet can be traced back to European languages, but in the operation of encoding Tunisian into Arabizi, there is no translation, but rather a transliteration, of the definite article or the inclusion of an article as a code-mixing element together with the name it defines, if this is an element belonging to another language system (this issue is discussed in section 4.3.2). A similar case to Tunisian Arabizi is represented by the Maltese orthography, which is the only standardised Neo-Arabic variety, and is strongly related to Tunisian, but is encoded using the Latin alphabet. Maltese definite NPs are represented by a geminated consonant in the case of nouns starting with coronal consonants, as in the NP /is-soppa/ of the following example (25) (Azzopardi-Alexander and Borg, 2013, 69).

(25) Il-kuċċarun

det.N li èawwad is-soppa det.N bih kollu tal-fidda,
'The-ladle he stirred the soup with is made entirely of silver'.

We can consider Arabizi to be a similar case, in which a Neo-Arabic variety is encoded through a Latin script which is used for another language that is strongly present within the culture. In order to observe if Tunisian Arabizi orthography (section 4.3) has a spontaneous settling tendency, we should consider the role of French in this process (section 4.3.2), in addition to the user profile and the text register (sections 4.3.3 and 4.4).

The same hypothesis can be researched by observing the Arabizi orthographic realisation of the Prepositional Phrase (PP), which diverges from the Arabic script, and which may be considered to be similar to that of French. Indeed, in Standard Arabic, depending on the preposition, a PP can be represented as a unique orthographic word. If the PP involves an articled noun, the orthographic word includes the three following morphemes: preposition (prep), determiner (det), and noun (N). In Tunisian Arabizi, the orthographic realisation of this syntactic structure is different, most often being encoded through the linking of the det to the prep and the prep-det compound separation from the N through a white space, as in the examples ( 11) and ( 14), which are both seen in chapter 1, which will be reported below.

In both examples, it is possible to observe how the prep-det compounds (bil and fil ) are separated from the Ns (respectively dlel and jografia), while the phonetic realisation corresponds to a unique compound, respectively: /b-@d-dlāl/ and /fi-ž-žuGr . āfya/.

As can be observed from the example, the second prep+det compound (ma+s in mas-sebè) presents a determiner (*al-), the /l / of which is graphically represented as s, being assimilated with the noun's initial consonant (sebè), which is a coronal. The similarity between Maltese and Tunisian Arabizi orthographies of the prep and det morphemes as a compound separated from the N allows us to suppose that the reason for this goes back to the multilingual environment facilitated by the Latin script. This clearly exists in the Maltese orthography, which still has clear traces of its Tunisian stratum, despite the Italian adstratum being immediately noticeable in sentences such as the following (example 27 (Caruana, 2009, 359)). European.

'The most important point of regional policy is to improve social and economic cohesion between European regions'.

Furthermore, in example (27), it is very clear that the nouns and adjectives used in this sentence are of Italian origin,8 while the invariable terms (or what Myers-Scotton would probably call 'system morphemes'), such as prepositions and articles, are Tunisian. The next section shall proceed with an observation of the orthographic reality of the prepositional phrase in Arabizi Tunisian. Our objective will be to verify whether the encoding in Latin characters facilitates an encoding that reflects the oral reality of Tunisian, or whether Arabizi still remains anchored to the writing tradition in Arabic characters, being above all a written form of encoding. The role of the influence of contact languages, particularly French, will also be discussed.

Prepositional Phrase Schemes

According to traditional Arabic grammar, prepositions are of two types, i.e. simple or lexically derived (Mion and D'Anna, 2021, 207). 'Simple' prepositions in Modern Standard Arabic can be proclitic, namely, bound to the noun which yields the PP, or independent:

1. /bi-/, /l(a/i)-/ and /ka-/ that link to the noun, or to the article that follows it;

2. /fī/, /Qan/, /Qalā/, /min/, /Pilā/, /munDu/ and /èattā/ that do not connect. 9

The PP in Tunisian Arabizi could be ideally realised through the following schemes, where the plus symbol (+) represents the orthographic concatenation:

1. When analysing the TArC data in order to investigate this hypothesis, it actually turns out that most prepositional syntagmas in Arabizi are realised through the [prep+det n] scheme, while the [prep det+n] pattern is less represented, as can be seen in table 4.1. 10

9 Except when followed by suffixed personal pronouns, as in lit. 'into me', lit. 'from me' and lit. 'on me'. 10 These analyses has been developed through the query tool system described in the previous paragraph, and the results of which, in terms of script output, are given in the appendix A.1.

in TArC are reported.

Patterns

Other Preps.

Occ ). This may seem obvious when reflecting on the fact that this preposition is always proclitic in MSA, but due to the fact that, in Tunisian Neo-Arabic, prepositions that are normally independent in MSA can behave as though they are proclitic, the opposite should also not be excluded. In addition, in section 4.3.2, we will observe how this orthographic rule becomes less obvious in the case of code-mixed NPs.

Having previously assessed [prep det n] as being insignificant, in terms of both frequency and in terms of the representativeness of the Tunisian oral or Arabic orthographic system, the percentages of the most frequently occurring prepositions should be included herein, in order of the highest occurrence in the [prep det+n] type of syntagm.

1. /fi/ : 36.6%;

2. /mtāQ/ : 24.4%;

3. /Qala/ : 21.9%;

4. /maQ/ : 17.1%.

The type of prepositions that occur in the orthographic realisation type [prep det+n] seems to further confirm the fact that this realisation type tends to reproduce the orthographic system in Arabic characters. In fact, excluding the preposition , which is typical of Neo-Arabic Tunisian but absent in MSA, the other prepositions in traditional Arabic orthography are encoded separately from the noun which they head within the prepositional phrase. 11

In general, the previous observations regarding the distribution of prepositions in syntagmas in which the preposition is independent of the determiner confirm, on the one hand, that a certain amount of attention is also being paid to the orthographic rules of MSA in the written encoding of Tunisian in Arabizi, and on the other hand, also confirm the fact that these types of orthographic realisations of the prepositional syntagma, in particular ([prep det+n]), are representative of a certain tendency to respect the Arabic traditional orthography. It should be noted, however, that this is a less frequent structure, compared with the first two in table 4.1 ([prep+det n] and

[prep+det+n]), where the distribution of the two prepositions appears to be even more interesting.

In the case of the first structure ([prep+det n]), in which the preposition is linked to the determiner to form a sort of articulated preposition, and which is assumed to be more representative of the Tunisian oral system, the situation is almost reversed. In fact, a good percentage of occurrences of the proclitic preposition , (52.7%) can be found here, which, despite always being lower than that of the preposition , (69%), is still typically independent. This factor can thus provide possible additional evidence to support our hypothesis. In addition, the preposition in the traditional Arabic orthography should rather impose a realisation of a [prep+det+n] type, while the 11 The prepositions and can both be phonetically and orthographically joined with a suffix pronoun in MSA (Mion and D'Anna, 2021, 213).

preposition should impose the [prep det+n] structure. Therefore, when compared to the less frequent orthographic realisations ([prep det+n] and ([prep det n]), this type of realisation ([prep+det n]) seems to be less constrained by the orthographic traditions represented by Arabic characters.

Finally, it is necessary to examine the structure [prep+det+n], which has been hypothesized to be located at an intermediate point on the continuum between greater representativeness of Tunisian orality and Arabic orthography. In this case, since the percentage of occurrences of the clitic preposition ( , 44.6%) greatly exceeds the number of occurrences of the independent preposition ( , 13%), it can be supposed that, in a binary system of analysis that contrasts the Tunisian orality with the MSA orthography, this type of orthographic realisation ([prep+det+n]) is positioned at an intermediate point on the continuum. However, we must also take into account the fact that the structure [prep+det+n] is more representative of the orthographic system of Standard Arabic than of Tunisian orality. Indeed, the structure type [prep+det+n] exactly represents the traditional structure that would be employed in prepositional phrases of written MSA involving the proclitic preposition . As for the pattern [prep+det+n], this represents a middle ground, as a possible solution in both languages (MSA and Tunisian), but only when the preposition is proclitic in the case of MSA. In contrast, in the case of Tunisian, annexation is possible for a larger number of prepositions.12 However, this last observation forces us to add two additional considerations into the mix:

1. The first is that surely the very nature of some prepositions, such as , imposes a specific realisation of the prepositional syntagm at the orthographic level, regardless of whether it is in Arabizi or in Tunisian encoded in Arabic characters.

2. The second is that considering the Tunisian linguistic reality to be a binary system is reductive in light of the points detailed in chapter 1. It is necessary to consider the idea that the tendency to graphically join the preposition to the determiner, separating them from the noun, may be a consequence of the phenomenon of contact with languages that present articulated prepositions, such as Italian and French.

With regard to the first consideration, the question of which prepositions occur most frequently in the various types of orthographic structures will now be investigated further; see the results of this investigation in the table below (table 4.3).

Patterns

Three most frequent prepositions % Other preps.

1.

[prep+n] (57.6%), ( 17 Except for the last of these, as expected, it appears that these prepositions have some influence on imposing an orthographic realisation for the PP. In patterns which tend to keep the preposition separate from what follows it, the prepositions are tonic, while conversely, in the other patterns, the prepositions are mainly proclitic. On the one hand, the role of the proclitic prepositions in restricting the possibilities of PP realisation in patterns 1, 3 and 4 seems evident. Nevertheless, in the case of a defined NP, the type of proclitic preposition does not really play a role in the choice of orthographic realisation among the possibilities represented by patterns 3 and 4. The same consideration applies to tonic prepositions employed in a PP involving a defined NP, i.e. in the choice between 5 and 6.

Regarding the second consideration, in addition to the phenomena of linguistic contact with European languages (an issue which will be discussed in section 4.3.2), there is one further question which requires consideration. This is the fact that the orthographic realisation of [prep+det n] may be a scriptural fallout of a morpho-syntactic phenomenon, or rather of the nature of the determinative morpheme. This morpheme appears in some Semitic languages dating back to the first millennium BC as a phonetic simplification of a deictic theme (Garbini and Durand, 1994, 102). According to [START_REF] Pennacchietti | Studi sui pronomi determinativi semitici[END_REF][START_REF] Pennacchietti | Ripercussioni sintattiche in conseguenza de'llintroduzione de'll articolo determinativo proclitico in semitico[END_REF] and [START_REF] Loprieno | Ancient Egyptian: a linguistic introduction[END_REF], the appearance of the determinative morpheme would have led to a reorganisation of the linguistic material (with relative and genitive functions) already present with consequent specialisation of the three functions [START_REF] Harning | The analytic genitive in the modern Arabic dialects[END_REF]Versteegh, 1984, 164, 98-99). 13The article in MSA appears completely standardised in al-as a definiteness mark (def) and a genitive mark in the case of specification complements (known as Construct State). On the contrary, in Tunisian, the determinative morpheme may overlap with @lli in the case of a definite reduced relative clause [START_REF] Jouini | Dependency relations in the syntactic structure of tunisian arabic[END_REF]. Indeed, @lli may be a complementiser or a relativiser, and in the latter case tends to merge with the preposition that precedes it, whether proclitic or independent, to form a single relative particle (such as m-@lli 'from which').14 Returning to the main topic of this thesis, it appears obvious that the role of the determiner seems to play a certain role in the orthographic realization of the Prepositional Phrase (PP).

In fact, looking at PPs lacking the determiner morpheme ([prep+n] and [prep n],

in table 4.4), the separation of the preposition from the noun, not their merging into a single compound, appears to be their main tendency.

Patterns Occ. %

[prep+n] 198 17%

[prep n] 969 83%

Tot 1,167 100% It seems plausible, therefore, that it is the det mark that plays a key role in these types of orthographic formations. Consequently, there is a reason to believe that the higher frequency of the [prep+det n] syntagm is due to the presence of det exerting an enclitic force on the preposition preceding it. This is exactly the same as in the case of @lli, and is probably caused by phonological reasons due to the shortness of their initial vowels, thus being related to the oral dimension of the language. What remains to be clarified is why Tunisian Arabizi instead prefers to leave the noun separated from the compound prep+det. In order to deepen the Arabizi encoding of the PP, a frequency analysis was developed in diachrony on TArC's data, allowing us to enter the second path of spontaneous settling tendencies.

Spontaneous Settling Trends

As outlined in Chapter 1, the Arabizi system is a recent invention, being born of the technological spread in Arabic countries at the end of the 1990s. However, since its inception until today, each Arabizi national variety should have been subject to a process of spontaneous settling, as it is a non-standardised system. This is also true for Tunisian Arabizi. Indeed, one type of evident spontaneous settling phenomena we can consider, as an example, is the use of arithmographemes. Regarding these, [START_REF] Bianchi | 3arabizi-when local arabic meets global english[END_REF] explains there are numerals which are employed as letters for 'hard-totransliterate' sounds. Since the inception of Arabizi encoding, all the numerals, except for the zero and the one, were employed to represent Tunisian phonemes.

Nowadays, the numerals that are still used are mostly 3, 5, 7, and 9, respectively for , , , and , as shown in table 4.5. The percentages show trends indicating widespread fluctuations throughout the period studied. However, when observing the first column (2005)(2006) in comparison with the last (2019-20 ), the values are quite similar. At the same time, however, after examining the central columns (2011-12 ) and (2013-14 ), in comparison with the first and the last, these show values that are diffusely lower, with the exception of the value reported for the arithmograph '2', the value of which is slightly higher than in the first column. In any case, although interesting to observe, the diachronic distribution of arithmographemes cannot provide an exhaustive answer to the question we posed at the beginning, that is, whether Tunisian Arabizi tends more towards the representation of orality or the respect of a graphic norm, the most influential exponent of which is represented by the encoding of Standard Arabic in Arabic characters. Therefore, in section 4.3.1, we will reexamine issues raised in the previous sections, in reference to the phenomenon of the orthographic realisation of the Prepositional Phrase (PP), which seems to offer some evidence in this regard. Therefore, section 4.3.2 will include observations on the code-switching phenomena in Arabizi, in order to understand if the PP scheme [prep+det n] could have been influenced by contact with European languages. Finally, in section 4.3.3, some diachronic and diastratic analyses will be presented, which were developed in order to understand if the data collected in TArC shows traces of the process described as koineisation by [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF] and previously outlined in sections 1.1.4 and 1.3.2.

Prepositional Phrase distribution

Regarding the linguistic elements of Arabizi that have been put under the magnifying glass of this quantitative analysis on TArC, it is now necessary to observe their diachronic distribution in the As with all the analyses presented in section 4.3, the analyses on TArC data have also been developed using the same query system, as outlined in section 4.1, and the results of which, in terms of script output, are given in appendix A.2.15

As shown in the previous section (table 4.6), the data for each year is not balanced, and therefore, in order to make the presented analysis as balanced as possible, the years have been grouped together. In this case, as in the example provided regarding arithmographemes (table 4.7), the aim is to observe the distribution of prepositional phrases over time, such that the most important factor is for the temporal groups to be contingent. The grouping performed is as shown in the following table (4.9). As can be seen in the above table (4.9), since the data for 2019 is much higher in quantity than the others, it was necessary to split it in half into two different groups.

The following analyses will present the distribution of the different prepositional phrase patterns over time, such that the table below (4.10) respectively reports the total occurrences of prepositional phrases for the year groups identified in table 4.9. These occurrences represent the total of the computations to be presented. Pearson's chi-squared test [START_REF] Pearson | on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling[END_REF][START_REF] Fisher | On the interpretation of χ 2 from contingency tables, and the calculation of p[END_REF] between the years and types of PPs, shows (with a p-value < 0.01) that the types are dependent from the years.

In particular, by observing the distribution of Type3 and Type4 over the years, it is possible to notice that the percentages of occurrence of structures of Type3 are always greater than those of Type4. A similar observation can be seen with regards to Type0 and Type1, where the second one (Type1) always accounts for a greater percentage in comparison to the first one (Type0). In both Type1 and Type3, the preposition is separated from the noun, and the only difference between them is that in the case of Type3, the determiner is present (and is merged with the preposition, rather than the noun). These could be considered to be two types, despite being the same type with a variation at the morphological level according to the presence or absence of the determiner.

Before the scope of the analysis is narrowed further in order to detect what, for native speakers and users, is more or less spontaneous/formal in the language (an issue we will address in the next section: 4.4), there is an additional matter to address. This is the encoding of the assimilated article in the case of defined nouns beginning with a consonant of the coronal type. This issue is related to both 'good' text encoding, as well as encoding tending towards the representation of orality or writing conventions in Arabic characters. However, the previous paragraph demonstrated that the most frequent PP pattern is the one that was previously identified as being the most representative of Tunisian orality. However, it remains to be understood why in Tunisian

Arabizi there is a tendency to graphically separate the 'articulated preposition' from the defined noun with a space. We believe that the phenomenon of determiner assimilation may be related to this tendency.

Regarding article assimilation in the presence of nouns beginning with coronals, when assessing the data summarised in table 4.12, it seems that whether NPs are part of a PP or not is irrelevant. In fact, NPs that show assimilation and those that do not show assimilation have frequencies of participation in PPs which are not too dissimilar (70 occurrences, namely 26%, for NPs with assimilation, and 65 occurrences, or 34%, for NPs without assimilation). Therefore, being part of a PP does not seem to have an influence on the realisation of assimilation. In contrast, when only electing NPs where nouns begin with a coronal (480 occurrences), it can be seen that the amount of noun-separated determiners is significantly higher in NPs that ignore assimilation on the orthographic level (almost 89%), compared to those that exhibit det's assimilation (3.7%). Pearson' chi-squared test, which is executed between NPs which include coronal phonemes at the beginning of an N (regardless of whether assimilation is rendered or not) and present in a space before the Ns, gives a p-value (< 0.01) which illustrates the relationship between the data. In the case of ignored-assimilation, the user therefore seems hardly inclined to orthographically respect the morpho-phonology of Tunisian, and in doing so, adopts a graphic coding strategy of NP and PP that includes it, namely, that of separating det from N with a space. However, from the number of occurrences of this, the tendency to respect the assimilation also at the orthographic level (269 occurrences) seems stronger than the tendency to ignore it, even if it is consistent (190 occurrences). Finally, in order to discover whether this was a type of diachronic variation, i.e., if the two modes of defined NP writing code, with and without assimilation, represent a trend for spontaneous settling over time, a diachronic analysis and Pearson's chi-squared test were performed for NPs that appear with an N beginning with a coronal over time. Results of the diachronic analysis are as shown in table 4.14, while

Pearson's chi-squared test of graphical assimilation and years gave a significant p-value.

Furthermore, the data was organised into years group with the aim of producing analyses which were as balanced as possible, and in the following table (4.13), we report the amount of Nominal Phrases for each group of years.

NPs 2005-2008 2009-11 2012-13 2014-16 2017-19 When examining the data in the table above, it appears that user habits are not fairly stable in terms of converging towards a graphical rendering of article assimilation, with some periods of trend reversal. In fact, an entire period (2009-2011 ) can be observed, in which the tendency to prefer the graphical rendering of assimilation is reversed, with an abrupt decrease in 2009, in correspondence with a peak in the values of the graphical non-representation of the article assimilation (from 2009 to 2012 inclusive). Immediately after the inverted-preference period, there is a notable period of balance, (2012-2013 ) and then a re-inversion in 2013.

The fact that this data can be correlated with sociopolitical events linked to the revolution is a thought that had obviously crossed our minds, given the identity value that this system of writing assumes, especially among the new generations, during this delicate phase of Tunisia's history. However, in order to test this hypothesis, native speakers should be surveyed, in order to evaluate the way in which they perceive this variable; we have decided to consider this as a possible basis for future work. 16 At the same time, it is also plausible that another social event influenced the practices of informal writing, and this is also connected to the above-mentioned sociopolitical facts (see chapter 1). The arrival of Facebook and its role in the contiguous mass diffusion of the Arabizi writing practice in those years was alluded to earlier. The fact that, in the last period alone, there seems to be an approximation of the percentages of these 16 The authors of the blogs collected in TArC have already expressed their support in this regard.

two modalities of graphical rendering for assimilation could be interpreted instead as a sign that the users consider it acceptable to have a system of written communication which is divergent from the 'scriptural norms' previously acquired. This could indicate that writing in Arabizi has become such a widespread practice that users no longer feel the need to eliminate any ambiguity from their texts and can ignore, for example, the 'correct' graphic rendering of the assimilated article. By adding to the data in this table, as well as the data in tables 4.15 and 4.16 types of users can be attributed to the two methods of performing graphical encoding for assimilation. In fact, the assimilation phenomenon seems to be diastratically influenced by both the users' age and their gender. Table 4.15 shows a preference for orthographic rendering of assimilation in the age range 35-50, and table 4.16 indicates that the same preference is mainly attributable to female users. Pearson's chi-squared test confirms that the variable is not independent from the age or the gender (with a p-value < 0.01).

Code-switching distribution

This work has documented how the variant of Tunisian Neo-Arabic, which has emerged as the national variety thanks to the mass media, coincides with that of the capital. We have also seen how this variety represents, at the diastratic level, a high social stratum with access to a French cultural and linguistic background, being rich in hybridisms or mechanism towards a French loan adaptation. As mentioned above in Chapter 1, [START_REF] Cerruti | Code switching'e teoria linguistica: la situazione italoromanza[END_REF] consider that it is better to keep hybridisms separate from code-switching (CS) phenomena, involving only the superficial linguistic system (i.e. words, morphemes and phonemes) and not the discourse, although they are noninstitutionalised manifestations of contact in use as much as CS (Cerruti and Regis, 2005, 193-194).17 Considering the preliminary character of the analyses we are going to present, we thought it appropriate to take into account different possibilities of interpretation of the phenomena resulting from linguistic contact detected in Tunisian Arabic. In order to maintain a simple expositive line, we have chosen to adopt Myers-Scotton's terminology, given the diffusion that his studies represent. The definition of code-switching (CS) adopted here is that of [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF], who states that the term is used to refer to alternations of linguistic varieties within the same sentence (intrasentential).

'CS is the selection by bilinguals/multilinguals of forms from two or more linguistic varieties in the same conversation. [...] Stretches of CS material may be inter-sentential (switches from one language to the other between sentences) or intrasentential (within the same sentence, from the single morpheme level to higher levels).'

Considering the two types of asymmetry involved in code-switching, i.e. the structural type and the content type, [START_REF] Myers-Scotton | Carol myers-scotton: Multiple voices: An introduction to bilingualism[END_REF] explains that, in a bilingual context, one language supplies the main grammatical frame for a clause containing words from both languages. [START_REF] Myers-Scotton | Common and uncommon ground: Social and structural factors in codeswitching[END_REF] also defines this structural-driver-language as the Matrix Language (ML), while the content-driven-language is referred to as the Embedded Language (EL), as already mentioned in section 1.3.3. The ML not only governs the form of a word selected from the EL lexicon, but also governs the struc-tural relationships between words within the sentence. A code-switching model for bilingual speech is the Matrix Language Model (MLF), while a model specialising in the morpheme types is the 4-M model (Myers-Scotton, 2006). MLF makes a distinction between content morphemes and system morphemes, in which the former are those which assign or receive thematic roles,18 while the latter are basically functional words, even if the two categories do not overlap completely. The 4-M model refines the MLF model by dividing system morphemes into three types: the early system morphemes, the bridge late system morphemes, and the outsider late system morphemes. In section 1.3.3, this topic was introduced through examples ( 16), ( 17) and ( 18 (17) S7i7, ama essa7el fi tounes hiya tasmya mta3 une zone, kima el cap bon..., /s . èīè, āma @s-sāè@l fi tūn@s h@ya t@smya mtāQ une det F zone,

N F
kīma @l-cap bon.../, 'That's right, but the Sahel in Tunisia, is a nomenclature of a zone, as the Cap Bon'.

These occurrences are particularly interesting when viewed in terms of the questions raised regarding determiner's role in the orthographic realisation of the Tunisian Arabizi NP and PP. Indeed, the occurrences of the Tunisian determiner followed by a foreign element were analysed in TArC. The aim was to observe the orthographic behaviour of the determiner, in case of a foreign N, and in particular when same is headed by a preposition. In fact, in the examples (18), two PPs can be observed. The first of these (f les chansons) is headed by a Tunisian preposition (/fi /), which is graphically separated by the determiner (les) and the noun (chansons), which are French elements. The second of these also presents a Tunisian preposition (/b-/), separated from the French indefinite noun (mention) which is followed by: the PP scheme. The second consideration is that which was observed in section 4.2.1, and which seems not to be respected in the case of a code-switched PP. Indeed, in example ( 18), the preposition /b-/ occurs separated from what follows it, which, in this specific case, is a foreign noun. It would be useful to deepen this consideration through quantitative analysis. In order to make this clear, in the analyses of the previous paragraphs, defined NPs containing nouns classified as foreign were purposely excluded. Instead, for these analyses, all the Tunisian det marks followed by a foreign noun in TArC (123 sentences) were selected, and 76 of these formed part of a PP.

In 73 occurrences (namely 59.3% of the 123 sentences and 96% of the 76 PPs), the preposition appears to be graphically joined with the determiner ([prep+det n]). There 20 23 My own translation: 'In the crew room with the commander himself, unrepeatable experience'. 24 My own translation: 'He brought me to a gallery where there are many works based mainly on

It is evident that, in the case of a code-switched PP, the most highly preferred solution is the following PP scheme: [prep+det n]. This finding is in line with the hypothesis regarding the general preference in Tunisian Arabizi being for the same scheme, which, as observed in previous paragraphs, could be influenced by the fact that Arabizi uses the Latin script. Likewise, the Latin script is associated with the French language, and its usage could lead users to also extend this PP scheme to the Tunisian not code-switched PPs observed in the previous sections. Unfortunately, we are unable to compare our data with statistics on a Tunisian corpus in the Arabic script, in order to check if this scheme is used more in Arabizi in comparison to Tunisian encoded in the Arabic script. 25 However, in order to support our observations, there are two other types of analyses which can be performed. The first is to check the typology of users' involved in code-switching practices. The second one is to observe whether there is any variation in the code-switching quantity and in the PP schemes among different genres of text. This second question will be further traversed in section 4.4, in order to verify whether the selection of an orthographic scheme is a matter regarding the degree of formality. Instead, we will outline the results of our diastratic question in the following table 4.17. However, since this also includes a diastratic analysis of code-switching, together with a diaphasic and diatopic one, it is impossible to proceed without first mentioning the studies that form the basis of the sociolinguistic approach to code-switching. The most prominent of these is the essay by Blom and Gumperz (1972), in which they address the issue of CS as a sophisticated strategy that speakers deploy to signal aspects of their ethnic and social identity. Among the various CS distinctions isolated by scholars, and destined to have great success, the 'we code' is particularly interesting. This represents the code used by speakers of a minority language for communication within their own ethnic community, and is opposed to the 'they code', which is used by the same speakers for communication with outsiders.

However, [START_REF] Auer | From codeswitching via language mixing to fused lects: Toward a dynamic typology of bilingual speech[END_REF] notes that the alternation of codes does not necessarily refer to a conversational-external opposition such as 'we-code/they-code', but that it is part of an emerging mixed code that exploits the dynamic relationship between the two source-languages (Auer, 1999, 119-120). When scrutinising the results of our analysis recovery and recycling; for example, earrings made from ice cream spoons, the plastic ones that we throw away, a cool painting made on a piece of iron (m5azza), an Afro portrait and (other) stuff, on a damaged wooden door'. 25 We may perform this analysis in the future. These latter percentages definitely seem to confirm that in the case of prepositional phrases that do not include code-switching elements, there is no variation in the sexual gender of users, unlike in the case of prepositional phrases that include code-switching elements. This finding takes on particular interest when juxtaposed with the conclusions of the previous section, in which the tendencies of the feminine gender for the orthographic rendering of assimilation are documented. Women seems to be more 'precise' in encoding, making use of the morpho-phonological phenomena of Tunisian in Arabizi, while at the same time tending to use a lot of French terminology in their daily exchanges, in which case they use a French-style encoding of PP ([prep+det n]). In fact, we would like to stress that the forums that make up TArC are generic forums and do not represent a particular sector-specific language. The fact that Tunisian women, and in particular those from Tunis, have a preference for the French language is certainly not new (Daoud, 2011, 15). Regarding the metaphorical-identity value that the French language and the Tunisian dialect may assume for this type of forum users, two hypotheses can be advanced. The first of these is based on the aforementioned study by Blom and Gumperz (1972), in which the two scholars identified a dual identity as the engine behind code-switching in a small Norwegian village. In this particular case, it was a urban identity in contrast to the identity of the inhabitants of a small village.

However, the authors themselves quickly realised the impossibility of establishing a simple and direct biunivocal relationship between the two, predefined between the use of a language and the ascription to a particular identity (Pasquandrea, 2007, 42). As addressed in section 1.1.3, it was unclear whether the status of the French language remained anchored to an elitist Bourguibian heritage or instead coincided with an everyday variation between urban and non-urban speeches. However, what our study has in common with the Norwegian case is that all participants in the interactions (in our case, young Tunisian women) seem to respect certain linguistic conventions imposed by a micro-social order. In contrast, the second hypothesis is that the two linguistic varieties used by a bilingual person go hand in hand and can be seen as mutually significant [START_REF] Woolard | Simultaneity and bivalency as strategies in bilingualism[END_REF] in creating a 'new' space for the bilinguals to occupy and utilise for self-positioning [START_REF] Finnis | Creating a 'new space': Code-switching among british-born greekcypriots in london[END_REF][START_REF] Georgakopoulou | Code-switching 'in site'for fantasizing identities: A case study of conventional uses of london greek cypriot[END_REF][START_REF] Nafa | Code-switching and social identity construction among arabic-english bilinguals: A stance perspective[END_REF]. In this specific situation, this idea of a new space may precisely exist as the space on women's forums, as anticipated in the chapter 1. 3. 27 We will address this question more fully in the analyses in the following paragraph (section 4.4).

Koineisation

In section 1.1.4, we introduced the topic of koineisation as a process that leads to the adoption of a national urban koiné, which tends to prevail in public spaces, while the vernacular which is typical of the place of origin is relegated to family communication [START_REF] Miller | Variation and changes in Arabic urban vernaculars[END_REF]. Koineisation is often a consequence of migrations and movements within the country. When referring to koiné, we imply contact between linguistic subsystems, although this contact does not always lead to the establishment of a koiné. As previously discussed in section 1.1.4, there are certain sociolinguistic conditions required for a koiné to emerge from this type of contact [START_REF] Siegel | Koines and koineization[END_REF]. Section 1.3.1 details how communication technology is supporting the spread of this national koiné, whether through television, radio or online channels, the diffusion of which is supported by the widespread use of smartphones. We then delved into the writing systems used for informal online communication in section 1.3.2, concluding with an overview, with no claim to exhaustiveness, of the Tunisian Arabizi system, in section 1.3.3.

However, there were still some questions which remained unanswered; these are the questions we asked in section 1.2.3, namely:

1. What kind of Tunisian idiom should we expect to find on social networks? 2. Will users employ their own local variant, or will they level out overly local features for the sake of inter-comprehension?

3. Will users choose an urban koiné specifically?

We will attempt to answer these questions through the TArC analyses outlined in this section. In order to do so, it is necessary to take, as discriminating macro-elements, those traditionally used for the classification between urban and Bedouin dialects, such as the double realisation of /q/, in [q] or [g], the evolution of diphthongs and some morpho-syntactic elements (such as the realisation of weak verbs, or variations within subject pronouns).

Qāf realization

With regard to the distinctive phonological features presented in table 1.1, the first point concerns the phonetic realisation of the uvular plosive phoneme /q/ in the voiceless [q] and/or the voiced [g]. This double pronunciation of /q/ is one of the main criteria for the classification of Tunisian idioms. As [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF] For further discussion of the typological category of village speech, reference should be made to [START_REF] Mion | Réflexions sur la catégorie des «parlers villageois» en arabe tunisien[END_REF], which highlights their mixed nature resulting from long interdialectal contact. As far as our study is concerned, what we want to verify is whether the diffusion of the urban dialect of Tunis has had an impact on the realisation of /q/ with consequent fallout at a graphemic level in the writing of Arabizi by users outside of Tunis. In order to carry out this data analysis, it is necessary to briefly describe how these two realisations of the same phoneme are distributed across the Tunisian territory. In the general classification of Tunisian idioms, village languages are found in the following areas: the Northwestern area (Bizerte countryside), the Sahel area and the Sfax countryside area (Mion, 2015, 270). According to the traditional classification, from the point of view of territorial distribution, the realisation of /q/ in [q] is a minority compared to that in [g], considering that all the rural Tunisia use the [g] phone, as well as some districts in cities on the coast, and Gabès (Saada, 1984, 28 'This double pronunciation constitutes the main criterion of distinction between the Tunisian languages, and this, for the uses themselves, which make the difference between « those who speak bil-qâla and those who speak bil-gâla », as well as for Ibn Khaldun who distinguished the « parlers des citadins » (with q) and « those of the Bedouins » (with g) and for William Marçais who had made of this double pronunciation one of the principal discrimina between « parlers citadins » (and village varieties), on the one hand and « parlers bédouins », on the other hand.' My own translation.

28). The bil-qala governorates are mainly Bizerte, Tunis, Sousse, Monastir, Mahdia, Sfax, and Kairouan. However, it should also be noted that within these governorates, there are many places where the realisation in [g] also appears (Mion, 2015, 271).

Considering that the intention of this research is to identify evidence of the koineisation process, regarding the phono-graphic phenomenon in question, let us take the traditional realisation as described in (Skik, 2003, 637-641) as an example, in which Skik states that he favours informants aged around fifty and older, in order to investigate the situation prior to the changes which occurred in the country, especially following independence in 1956. Skik, while stressing the non-existence of clear boundaries and of governorates knowing a single pronunciation, identifies as bil-qala governorates: Bizerte, Ariana, Tunis, Zaghouan, Nabeul, Kairouan, Sousse, Monastir, Mahdia, and Sfax. The rest of the governorates are presented as bil-gala. Additionally, [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF] states that the realisation in [q] is far less than that in [g], and that there are variations on the level of sexual gender, between the two realisations, the study of which should be investigated further. In spite of the imprecise nature of an analysis carried out by observing governorates instead of villages or towns, the result of our analysis, as shown in the table below (table 4.21), reveals an important fact, i.e., that today's situation, from a quantitative point of view, of realisations in q or g, seems to be reversed, compared to that presented by [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF]. In fact, at the top of the column we can see that out of the total data taken into consideration (excluding Tunis, as the model of the koiné whose diffusion we want to observe), the percentage of realisations in q or 9 (81.03%) is significantly higher than the realisation in g (18.96%). Moreover, from the same table, it can also be observed that the largest contribution of realisations in q actually comes from the governorates classified by [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF] as the urban type, as shown in the upper part of the table. However, in the governorates classified by [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF] as predominantly bil-gala areas, the realisation in q is not negligible. On the contrary, with regard to the 18.96% of realisations in g, the majority of examples are from the governorate of Gabès (representative of the Bedouin Sulaymite typology, and well-known bil-gala-speech exponent (Saada, 1984, 28)), but 36.37% of that 18.96% is made up of realisations from the predominantly bil-qala type governorates. Concerning the situation in the capital, this seems to have instead reconfirmed the data which indicates that most citizens prefer the realisation in q or 9. These results seem to confirm today's situation, compared to the pre-independence one, in which an ongoing process of koineization is being based on the model of the capital. In fact, Pearson's chi-squared test between urban (excluding Tunis) and non-urban varieties and the realisation of the phoneme /q/, according to our analysis shows a data relation, as well as for Tunis data (p-value). The following analysis was carried out by taking up the suggestion of [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF] regarding the need to investigate the gender issue relating to the realisation of /q/. Table 4.25 is considered to show all the instances of /q/ which are written as q or 9, in all the governorates (except for Tunis, which is depicted in 4.26).

Regarding the distribution of the users who produced these graphemic realisations, their gender seems to be quite balanced, with a split of 43% of males and the 57% of females. Instead, the g grapheme seems to be mostly preferred by male users (90% of the bil-gala productions), while only 2.7% were produced by female users. The bil-qala realisation is produced equally by men and women, but only 26% of the male users wrote down the /q/ as g (corresponding to 90% of the bil-gala production in table 4.25). In order to prove our analyses, Pearson's chi-squared test was performed, which gave a p-value of less than 0.01. Below is the contigency table for data excluding Tunis (4.23) and including Tunis (4.24). In fact, we have also added data coming from Tunis, to all the other data, and performed Pearson's chi-squared test between gender and /q/ realisation. The test again gave a positive p-value.

Male Female Tot

[q] 87 114 201

[g] 28 3 31

Tot 115 117 In table 4.26, in light of the fact that the majority of users prefer the q or 9 encoding (94.68% of table 4.22), it can be observed that, among all of the male users from Tunis, most of them (approximately 94%) prefer the q or 9 graphemes. The situation appears similar for female users (approximately 97%). These results are contradictory to the assertions of [START_REF] Skik | La prononciation de qâf arabe en tunisie[END_REF], despite it being well known that bil-gala speaking is a practice typically associated with men (and coming from the countryside). It is possible that this phonological phenomenon is more alive in rooted linguistic ideologies than in contemporary reality, but at the same time, the two may feed off of each other. As an example, it is worth mentioning the linguistic practices related to the world of rap, a predominantly masculine world, where, according to the linguistic ideology of native speakers, the realisation of bil-gala should be predominant. However, based on our analysis, it turns out that the exact opposite is in fact true.29

bil-qala bil-gala 91.76% 8.24% Table 4.27: Analysis of /q/ encoding in rap data.

It will be interesting to observe the score assigned by Pearson's chi-squared test, to see if there is correlation between the realisation of /q/ and textual genres (besides the musical genre of rap). This will be dealt with in the next section (4.4).

Diphthongs

Among the features to be observed, traditionally considered distinctive between conservative and innovative systems, is the evolution of etymological diphthongs. The diphthongs /ay/ and /aw/ are in fact preserved (in southern Tunisian dialects (Durand, 2007, 248)). However, there is a prevailing tendency for the monophtongisation of */ay/ into ī or ē and of */aw/ into ū or ō (Saada, 1984, 33-38). According to [START_REF] Gibson | Dialect levelling in tunisian arabic: towards a new spoken standard[END_REF], the most conservative systems, with diphthong realisations, are found in Sfax, Nabeul, and in the speech of old women in Tunis. Regarding the monophtongisation, the five-long-vowel system (ā, ē, ī, ō, ū) is found in several Hilali systems, but in particular in the Sahel village dialects (Mion, 2015, 271), while the three long vowel system (ā, ī, ū) is found in pre-Hilali systems, such as in Tunis and Sousse [START_REF] Mion | Réflexions sur la catégorie des «parlers villageois» en arabe tunisien[END_REF][START_REF] Durand | L'arabo di tunisi. note di dialettologia comparata. Dirāsāt Aryūliyya[END_REF][START_REF] Gibson | Dialect levelling in tunisian arabic: towards a new spoken standard[END_REF].

In this regard, the analysis can inform us of certain aspects, such as the general percentage of the occurrence of diphthongs and monophthongation. However, the results of the distribution among the governorates cannot be considered to be perfectly informative; in fact, in order to simplify the automatic detection of patterns in the corpus, we adopted exclusion strategies that led to a reduction in the amount of data, particularly for diphthongs, and this amount cannot be considered sufficient for statistical analysis. Regarding the tokens that we excluded from our analysis, these are mainly the proper nouns (such as Sayf or Nawres), loanwords, and all verbs in order to avoid matches with first radical weak verbs, such as /ywarrī-hum/, 'he shows to them'. the northwest (Jendouba), the Sahel (Sousse), and the south (Sfax, Gafsa, and Medenine). However, the same governorates also, almost always, show a good percentage of diphthong reduction, e.g., Bizerte (17.57%), Jendouba (5.41%), Sousse (10.81%), Sfax (6.76%), Gafsa (5.41%), and Medenine (6.76%). Finally, considering the high percentages of monophtongisation found for Bizerte, Sousse, Medenine, Monastir, Sfax and Ben Arous, we can conclude that, at least according to this data, it seems possible to hypothesise the diffusion of the monophtongisation system starting from the major urban centers of the country (Tunis, Bizerte, Sousse and Sfax). 4.28). We must therefore consider it plausible that such a result can only be returned by data coming from social media, where evidently there is a certain tendency to speak according to a

Gender opposition at 2 nd singular person in verbs and pronouns

There are two more analyses that need to be tackled in order to check the influence of Tunis Arabic on the rest of the country. In this case, two morphological issues were chosen to balance the analysis. The first of these is the gender opposition for the 2nd person singular, for both the independent personal pronoun system and verbal conjugation. In general, for urban languages (such as Sousse and Kairouane), the dialect of Tunis does not feature any gender opposition. However, across the rest of the country, according to the traditional description, it is possible to find, in the village or Bedouin dialects, oppositions both at the pronominal and verbal level [START_REF] Mion | Réflexions sur la catégorie des «parlers villageois» en arabe tunisien[END_REF]La Rosa, 2021, 273, 13-14) Urban style Gender opposition 98.32% 1.68% The results have shown, in tables (4.32 and 4.33), a clear tendency for Tunisian systems to follow the Tunis model, where the occurrences of gender opposition are minimal (and restricted to the isolated pronominal), at least in the context of informal online writing.

Realisation of -w plural morpheme in weak verbs

The second phenomenon of a morphological nature is the realisation of the plural in weak verbs. In fact, the treatment of the plural morpheme -ū in the form of the perfect and imperfect is different in the pre-hilalic and hilalic systems, where, for the verb mšā, in the former case we have mšāw-y@mšīw 'to go', while in the latter we have mšūy@mšū (Mion, 2015, 272). By inspecting the data and comparing the results obtained for the sum of all the governorates (excluding that of Tunis) with that of Tunis, a trend opposite to that of the previous section can clearly be seen. In the tables below (4.34 and 4.35), it can be seen that there is no convergence of the writing systems of the various governorates towards the Tunis model. In the case of the gender opposition in the second person singular, users of online platforms, who are not from Tunis, are inclined to adopt an urban system. This tendency may be reinforced by the fact that they are addressing someone who is most likely not in their circle of friends and whom they are addressing directly. Otherwise, the purpose of convergence towards a koiné might also be to reduce the ambiguity of discourse. The same need to tend toward a shared model does not seem to be felt in the case of using plural person verbs, perhaps because it is something that the user pays less attention to, is more difficult to 'level' on the koiné model, or is perhaps simply less perceived as a source of ambiguity for discussion in DNW. In conclusion, we can consider the hypothesis of the diffusion of the Tunis model in a koineisation process to be valid, which concerns the world of DNW. What remains to be clarified is the degree of spontaneity/formality that such a koiné represents, a topic which will be addressed in the next section.

Continuum of degree of formality

The last research path specifically concerns the topic of text specificity, and in particular aims to identify the differences between the genre of a text, namely social networks, forums and blogs, which are the three genres covered by TArC texts. In fact, the third hypothesis sees these three genres occupying different positions along a formality continuum. Typically, written speech is considered to be more formal than spoken speech, but, nowadays, with the advent of written messaging and social platforms such as Facebook, this paradigm appears to be obsolete. In the DNW, users employ writing, which is typically associated with formality, to communicate messages that are typically informal (Sullivan, 2017, 30). As already covered in section 1.3.1, technology supports writing in whatever form is desired; users are not bound by any standards and are free to use whatever channels they choose to create and express social or cultural attitudes and identities, moving from standard to nonstandard varieties and back again in a more flexible way than is generally possible in a non-electronic written context. However, a text's formality is connected to the degree of spontaneity of the text, and accordingly social network texts are less formal in comparison to those of blogs, and that forums should find their place in the between. In order to analyse the formality degree we will observe some general phenomena typical of spontaneous text, such as punctuation, the use of emoticons and interjections, in the three textual genres in section 4.4.1.

Next, in section 4.4.2, we will test whether our hypothesis about the distribution of prepositional patterns along a continuum of formality coincides with the distribution of the three textual genres along the same continuum. Also in this case, the query tools used are available on the mentioned website and the script output can be find in the appendix A.3.

Text genre general analysis

In order to be able to base our vision on concrete data, of a continuum of formality on which to place the data of Social Networks and Blogs as extremes, and Forums as intermediate types of texts, we have identified three very simple parameters among the DNW characteristics presented by [START_REF] Thurlow | The language of text-messaging[END_REF] and have discussed them in section 1.3.3. These consist of the number of occurrences of interjections, the number of elements classified as emotags in our corpus (emoticons, smileys, etc.), and the number of occurrences of punctuation marks at the end of sentences. With regard to the first two, these are parameters, the size of which will be directly proportional to the value of the spontaneity of the text, while with regard to the last parameter, this will be inversely proportional to the degree of spontaneity. The data presented in table 4.36 fully supports the hypothesis regarding the positions that the three textual types take on the continuum of formality based on the degree of spontaneity of the implied texts. In fact, when observing the first two parameters, i.e. the number of interjections and emotags, it is possible to see that these are numerous in texts coming from social networks and forums. In the case of forums, the percentages decrease, getting closer to those found in blog texts. When instead observing the parameter considered in the last analysis, i.e. the punctuation at the end of a sentence, it is clear to see that, in the case of texts coming from social networks, there are very poor values (14.32%), while in the case of forums and blogs, the values are much higher (approximately 64% and 79% respectively). We can also say that forum texts are located almost halfway between the two extremes of the continuum in terms of the first parameter (the number of interjections), while the second and third are closer to the blog typology than to the social network typology.

Nominal Phrase distribution

Considering the possible combinations mentioned at the end of the previous paragraph, our hypothesis is that, in the case of a Prepositional Phrase (PP), which includes the article (det), the combinations are arranged along a continuum from the most representative of Tunisian orality ([prep+det n]) to the most representative of the orthographic system of Standard Arabic ([prep det+n]).

Therefore, section 4.3.1 will rejoin the discussion on taking up the phenomenon of the orthographic realisation of the Prepositional Phrase (PP), which seems to offer some evidence in this regard. Therefore, in section 4.3.2, code-switching phenomena was observed in Arabizi, in order to prove its role in the preference of the PP scheme [prep+det n]. Indeed, by observing the data, it was possible to assert that Arabizi encoded by the Latin alphabet seems to be have been influenced by contact with European languages.

In table 4.12, it can be seen that among the defined NPs that witness a det's assimilation, only 10% of the defined NPs realise the pattern [det n]. Therefore, the tendency seems to be to graphically realise the defined NP as [det+n]. Instead, in the cases of defined NPs that do not respect article assimilation orthographic rules, the prevailing orthographic tendency seems to be to coincide with [det n]. In simple terms, it seems that if the article and noun do not 'merge' on the phonetic level, then they should not do so on the graphemic level either. Moreover, table 4.37 shows that this 'rule' of encoding the defined NP of Arabizi seems to be more or less respected according to the degree of formality of the textual genre.

In fact, the data confirms that separating det and N with a space is an orthographic choice which is more consistent (61.5%) in the contexts of more controlled writing con-

Conclusions

In order to carry out these analyses independently, but at the same time ensure that they are interrelated, a phenomenon of a syntactic nature with orthographic fallout, which is very frequent in Tunisian Arabizi, was selected. This is the definite prepositional phrase, the realisation of which has been defined, just for descriptive purposes, as divergent from that of Standard Arabic. In fact, in Tunisian Arabic, it is possible to find orthographic patterns that represent a kind of articulated preposition, where the article is graphically joined to the preposition that precedes it, whether it is proclitic or not. The orthographic rendering of the articulated preposition in Tunisian Arabic appears similar to the modality found in European languages and different from the typical realisation of it in Standard Arabic, where the two elements are graphically joined only in the presence of some proclitic prepositions. By analysing the frequency of the various prepositional patterns, we have come to the conclusion that the patterns are arranged along a continuum ranging from the maximum representation of the Tunisian oral system to that of the writing habits acquired by writing in Arabic characters.

At this point, the role that linguistic contact between French and Tunisian Arabic plays in these graphic realisations of the prepositional phrase have also been considered, as well as the very nature of the Tunisian definiteness mark, which, in some contexts, seems to overlap with the relative mark. Having observed a key role of the definiteness mark in the formation of these prepositional patterns, we decided to investigate them further through diachronic analysis of the TArC data. This naturally led us to the scope of the second question related to settling trends in Arabizi writing practices.

The TArC data contain texts ranging from 2005 to 2020; however, the amounts of data for each year are not equivalent to each other. As such, other strategies were put in place in order to ensure balanced analyses, for example, through the use of statistical tests such as Pearson's chi-squared test. In order to give an example in this regard, we carried out a small analysis on one of the most friendly features of Arabizi, namely the use of numerals as graphemes, which were previously defined as arithmographs. We then continued our preliminary diachronic analyses of the distribution of prepositional phrase patterns. Through these analyses, we found a correlation between two types of prepositional patterns, the [prep n] type and the [prep+det n] type. The second type is what we have defined as the representational pole of the Tunisian oral system in the analyses related to quasi-orality. Given the correlations found between the two patterns in the analyses related to settling trends, we were also able to define these two patterns as being the same type of prepositional pattern (where the noun is separated from the preposition) with variations on the morphological level based on the presence or absence of the definiteness mark.

In order to investigate this issue further by scrutinising the morpho-syntactic intersection with the phonetic plane, we then looked at whether the presence of coronal phonemes played a role in the realisation of one pattern rather than another. In fact, we have often defined orthographic variation within the Arabizi system as providing a certain degree of freedom for the writer in terms of representing or not representing certain linguistic phenomena. One of the phenomena in question is that of article assimilation in the presence of nouns beginning with a coronal phoneme. Tunisian Arabizi writers sometimes do not represent article assimilation. Therefore, we observed that in the distribution of prepositional phrases containing nouns that should assimilate the article (because they begin with a coronal phoneme), when assimilation is not graphically represented, the noun is often separated from what precedes it, regardless of whether this is an article or preposition (with a frequency of 88.9%). One of the plausible reasons for this is that the writer, in not rendering the assimilation graphically, feels the need to adopt a coherent behaviour at the orthographic level, keeping separate, at the graphic level, the elements that would not produce assimilation at the phonological level, which he has, for some reason, chosen not to render at the orthographic level.

Diachronic analyses of the graphical rendering of assimilation were also conducted, so as to find trends that could only be explained by interviewing native speakers. In this regard, we have already stated our interest in conducting interviews dedicated to the topic with bloggers with whom we have already made contact. In fact, the tendency in research over the years seems to be mainly towards graphically rendering the assimilation; however, this trend has seemed to undergo a sudden reversal over recent years, due to the spread of social networks coinciding with the political upsets of the Arab revolutions. With regard to the graphic rendering of assimilation, we also found a tendency to maintain a type of handwriting which is coherent with the phonetic plan in the 35-50 age group (80%) and among female writers (77.4%). On the other hand, those of the male gender seemed to care less (24.7%) about a 'phonetically correct' graphic rendering. We have therefore concluded that the graphical rendering of assimilation seems to be a diastratically influenced issue.

Finally, we have also observed the influence of the French language on these spelling practices. Indeed, it is clear to see the distribution of prepositional phrases that include marked nominal syntagmas at the code-switching level. Again, and especially in this case, the [prep+det n] pattern was found with a very high frequency. We then conducted these analyses by taking into consideration the type of users involved, as well as the textual genres. It appeared that those making the most use of intrasentential code-switching are female forums users.

As mentioned earlier in this chapter, these findings reinforce the previous observation regarding the female gender's preference for an orthographic rendering of assimilation. Women seem to be more 'precise' in encoding the morpho-phonological phenomena of Tunisian, and at the same time, also tend to use a lot of French terminology in their everyday exchanges; in this case, using a 'French-style' encoding of the prepositional phrase ([prep+det n]). We also mentioned that this is in line with already widespread linguistic observations regarding the language of women, particularly from Tunis, and their predilection for French. We also speculated about the identity significance that this space, i.e. the forum, has for women's social groups, referring to the meaning of 'new communicative space' as exposed by [START_REF] Woolard | Simultaneity and bivalency as strategies in bilingualism[END_REF].

Undoubtedly this issue deserves further investigation which we reserve for the future, indeed, the kind of contact phenomena discussed here could fall within the sphere of code-switching, but it will be interesting to separate such elements from nonce borrowing and the occasional insertion of isolated lexical elements.

With regard to the questions posed about the type of language that can be encountered on social networks, and from which the path of analysis inherent to the settling trends of Tunisian Arabizi was then born, we chose to observe the distribution of some specific features in particular. This included those traditionally considered to be characteristics of diatopic variation within Tunisian Arabic. In fact, we also discussed the hypothesis regarding whether or not, within Arabizi practices, a language influenced by the koiné of Tunis can be identified. We then observed the realisation of the phoneme /qāf/ in the data from the different governorates collected in TArC, before comparing them with those from Tunis. Our data seemed to confirm the presence of an ongoing process of koineisation based on the model of the capital, which have already been attested to in previous studies to which we refer in the chapter.

Given that hypotheses have been raised in the past about a diastratic variation of this phoneme realisation, an analysis was also performed for another issue by observing the variations in sexual genders. These analyses highlighted the strong preference (of both genders) for the orthographic realisation of /q/ in q or 9 in the governorate of Tunis, and a clear preference of the male gender for the orthographic realisation in g in all other governorates. The result obtained in Tunis was at odds with the fact that, in general, the realisation in q is considered to be a feminine practice. This led us to a theory that perhaps this phonological phenomenon is more alive in rooted linguistic ideologies than in contemporary reality. Another of the characteristics observed in the analyses related to the issue of the koiné was that of the realisation of diphthongs.

As stated earlier in the chapter, we quickly realized, through our analysis, that this can only inform us of certain aspects such as the general percentage occurrence of diphthongs and monotone. However, we cannot consider the results of the distribution across governorates as being completely accurate. In fact, in order to simplify the automatic detection of examples in the corpus, we adopted exclusion strategies that led to a reduction in the amount of data, particularly for diphthongs, and this amount was not considered sufficiently large for statistical analysis. However, we report the results of these analyses, as well as our intention to extend this field of study in the future. It appears that the trend for the most part, both in Tunis and in all the other governorates, is that of reducing the diphthong to a long vowel (monophtongisation).

What left us a little surprised was the fact that the diastratic analysis shows the clear preference of the male gender (78.8%) compared to the female gender (21.3%) for the maintenance of the diphthong. It must be specified that the conservation of diphthongs by women in the old city of Tunis is a phenomenon described many years ago, which would certainly have to be verified to be sure that it still exists. Thus, the data may not be surprising at all, since women may have changed to the reduction of diphthongs a while ago and the graphic rendering of diphthongs (not equivalent to the phonetic one) may have entirely different reasons. This intriguing discovery is worthy of further study in the future. In order to conclude the analysis on the process of koineisation, we have finally observed two other variables, namely that of gender opposition and that of the realisation of the morpheme -w of the plural in weak verbs. The first shows the clear tendency of the varieties of Tunisian excluding that of Tunis to overlap with the Tunis model, the second instead seems to maintain a certain divergence between the two, in which Tunis remains clearly urban, while the sum of the other governorates exhibits the opposite trend.

Once we had highlighted these dynamics, we wanted to analyse the formality continuum theory exposed above as the third analysis path. In a very simple way, we identified informative text features about the degree of formality and then performed analysis on them. These were the presence of interjections, smileys or emoticons, and final punctuation marks. The first two are typical of informal texts, while the latter is typical of more controlled texts. Our analyses confirmed the distribution of the three textual genres on the continuum as assumed, i.e. the blog genre being the more formal and the opposite pole of the social network genre.

Finally, once this distribution of textual genres was confirmed, we could reconnect to the observations made about the most frequent prepositional pattern in Arabizi

Tunisian ([prep(+det) n]), on the basis of its distribution in these three textual genres. What we had previously observed with regards to this pattern was its diastratic influenced variation, as it is very much in use among female forum users, in the case of prepositional phrases marked by code-switching. We had therefore hypothesised its use as an orthographic choice, even in the case of nominal and prepositional phrases that include nouns that should have produced an assimilation of the article. In fact, in the case where such assimilation was not rendered graphically, with a high percentage (approximately 89%), we had encountered precisely this type of prepositional pattern with an isolated noun. In the end, the analysis on textual genres, which showed a high percentage of the same scheme in texts coming from blogs (61.5%), seems to definitively confirm the hypothesis that this scheme presupposes a conscious choice of the writer, i.e. an orthographic choice made in a controlled textual context.

General Conclusions

This work aims to investigate Tunisian Arabic while also providing a response to the lack of tools to support research on Tunisian Arabic. In particular was the objective of building a corpus suitable for various type of linguistic analyses, hence the many levels of annotation with which we have provided same. We particularly narrowed down our outlook to a specific variety of Tunisian Arabic, which is that employed for digital communications and identity sharing, namely what we defined as Digital Networked

Writing. Moreover, we collected texts encoded in the typical writing system dedicated to this context, namely Arabizi.

In Chapter 1, we introduced the multilingual complexity of Tunisian, by providing the reader with some fundamental keys with which to capture the possible implications behind the use of this writing system. Among these key points, we considered the history of the country (as summarised in section 1.1.1), the traditional dialectological classification of Tunisian Arabic (outlined in section 1.1.2), the historical and modern interpretation of the distribution of different linguistic entities across the territory (exposed in section 1.1.3). The topic of multilingualism, specifically, has served as a gateway for subsequent questions about the emergence of an urban variety over the others (discussed in section 1.1.4). However, this emergence has an intermediate character between a spontaneous and a driven event, since this urban variety did not undergo a policy of standardisation. However, the diffusion of the Tunisian koiné was effected through the means of the mass media. After briefly delving into the issues regarding language standardisation in order to draw a distinction from the spontaneous emergence of language varieties (section 1.2), we entered the heart of our research topic by describing the world of new communication spaces that has emerged thanks to Internet diffusion (in section 1.3). Here, in fact, we also mentioned the diffusion of Tunisian in Computer-Mediated Communication (CMC) (in section 1.3.1), and in particular the dual mode of writing, digraphia (see section 1.3.2). We finally presented the main characteristics of Tunisian Arabizi (in section 1. 3.3).

Throughout the first chapter, we began to present the questions that motivated our research on the Tunisian Arabizi system. These questions were related to its very nature as a spontaneous writing system. Since the characteristic of spontaneity plays a role in the degree of encoding freedom left to the native user, we wondered how much the user could be conditioned at the time of writing. We considered whether a condition could have been posed by previously learned scriptural practices. In fact, before the spread of Arabizi, Tunisian was mainly encoded in Arabic script, and this practice may have influenced the way in which Tunisian was encoded in Arabizi. At the same time, we also considered the role of French scriptural habits, as French was also a very present language in the country. An alternative to these two possibilities that still includes the influence of earlier writing practices is instead that Arabizi is somehow not conditioned by any of the above, and is therefore oriented towards the representation of Tunisian orality. We referred to this line of research in terms of quasi-orality in our analyses. A second question we posed concerned the possibility of identifying the degree of settling that Arabizi has achieved nowadays, as a non-standardised system, but which is still widespread and used daily. We referred to this line of research as the question of settling trends in our analyses. A third question, which also relates to the former, is whether Arabizi varies as the degree of formality of the communication channel varies. Indeed, the degree of variability of Arabizi can be related to the degree of its spontaneity and that same is linked to the degree of formality of the context into which the text was inserted. In order to carry out our preliminary analysis in this regard, we have therefore drawn a continuum of formality within the context of Digital Networked Writing (DNW). In the case of our data, the two poles of this continuum consist of texts coming from blogs, featuring a high degree of formality, and texts coming from social networks such as Facebook, with a low degree of formality. We considered the possibility of viewing texts from forums as representing an intermediate degree. In fact, these texts often presented features somewhere in between the previous two types.

The issues around which we conducted the analyses had already been anticipated, in order to motivate the choices made when building the corpus. In fact, in order to be able to carry out these analyses, a corpus of a certain size was required, so that it could represent the different textual genres mentioned above. We also needed a corpus that could make analyses in diachrony and diatopy possible, and that, in general, could support the observation of diastratic variations. As such, in chapter 2, after a brief introduction (section 2.1), we outlined our methodology. First of all, we described the structural characteristics of DNW and how it is addressed by linguistic research (in section 2.2). We then went into details about the good practices to be observed when building linguistic corpora (in section 2.3). In the end, in order to speed up the process, and in order to ensure the reproducibility of the methodology adopted, but also to extend the pool of usefulness of both the corpus and the procedure itself, we opted for the use of deep learning techniques. The usefulness of the procedure will be discussed below with regard to future projects. In addition, we also provided some useful information for understanding the techniques employed to build our corpus, while the procedure itself was described in the following chapter (the techniques overview; see section 2.4). In the end, we reported the state-of-the-art as having two purposes (in section 2.5). The first aim was to share the information concerning the various types of work carried out with a methodology similar to ours on Modern Standard Arabic (MSA) and dialectal Arabic. In fact, in the first phase, the processing of the latter underwent the adaptation of tools created for MSA. The second objective of the state-of-the-art was to highlight the lack of resources available to support the research on Tunisian Arabic in this research area (and in general on Maghrebi ).

Chapter 3 dealt with the specific operations, which were described step by step, that led us to the TArC realisation. The chapter traced the path back to the beginning, starting with the collection of data and the decisions made for selecting and collecting metadata of the texts (in section 3.1). We also described the stages of semi-automatic annotation of the corpus into its annotation layers: classification at word-level, transliteration into Arabic characters, tokenisation, Part-of-Speech tagging, and lemmatisation. We also discussed the experiments that led us to identify the best strategies for achieving our goals moving from a first phase (outlined in section 3.2), to the second (addressed in section 3.3). Finally, we described the result of the second phase pro-cedure, which consists of the multi-task sequence prediction architecture. This was the tool which was built to produce the different annotation layers that make up the Tunisian Arabish Corpus (TArC) from the Arabizi text (the system is described in section 3.4). The other result of the second annotation phase is indeed the corpus itself. This is described, along with information about the amount of data and metadata which it includes (in section 3.5).

Finally, in Chapter 4, after a brief overview of the query tools employed for our analyses (in section 4.1), we addressed the preliminary investigation on the questions mentioned above. Indeed, the analyses were aimed at delineating the nature of the corpus itself and at undertaking computational-linguistic strategies to observe the linguistic reality of Tunisian Arabizi along the three paths identified. These are the quasiorality traits of Tunisian Arabizi observations (in section 4.2), the Arabizi spontaneous settling trends (in section 4.3), and the continuum of formality degree hypothesis investigation. The latter has been performed by observing the Arabizi writing regularities among different textual genres (in section 4.4). We are reminded that each of these analyses is reproducible due to the provision of the small scripts used to perform them.

In addition, information regarding the linguistic data which can be observed using the scripts are available in the appendix (A.1).

It was very important to conduct these initial analyses in order to highlight certain trends, confirm our hypotheses or better understand the Tunisian Arabizi reality. This experience helped us clear our future path and understand how to proceed with our next research. In fact, in a number of cases, we even noted the need for further observation of issues raised by our analyses. As an example, we would like to extend the data by directing further data collection towards texts from specific governorates in Tunisia, so that we can address the diatopic analyses in greater detail. Furthermore, in the initial data collection phase, we had chosen not to impose our views regarding the distribution of Tunisian features, and we did not impose any conditions regarding the geographic origins of the texts. At the same time, in order to do this, it was necessary to develop systems to automatically identify provenance based on textual characteristics. This is an area of research known as Arabic Dialect Identification (ADI), on which considerable progress has been made in recent years (Abdul-Mageed et al., 2021).

In addition to investigating the issues which have already been raised, we would also like to conduct further comparative analyses between the two writing systems available for Tunisian Arabic, which we had already begun to consider doing when collecting the rap data. In fact, we would like to retrace the three analysis paths included in this work in a comparative modality, in order to extend the analyses validity in terms of completeness from the point of view of the orthographic systems in use for Tunisian Arabic.

At the same time, one of our next projects is to further extend the corpus via the same multi-task system built to generate TArC annotations. For example, an additional annotation level, which we would like to provide for the TArC, is a layer consisting of the type of transcription traditionally used in dialectology. By adding this layer TArC data could be integrated with other tools, such as dictionaries and glossaries.

Regarding the multi-task system, we have already considered how to improve its performances by integrating TArC data with the recently released MADAR corpus in CODA orthography. In fact, we have already demonstrated the usefulness of MADAR data in improving the performance of the multi-task system. Now that MADAR is available in the same type of encoding employed in TArC, we are able to exploit all the data it contains without any of the previous encoding normalising effort.

Both the multi-task system and the TArC data have been made available to the scientific community. These constitute useful tools for reproducing our methodology, as well as for quantitatively expanding the TArC or generating other corpora twins of the TArC, i.e. by collecting different genres of texts. As future work, we also consider the following possibility: we could even exploit the potential of the MADAR corpus further, as a parallel corpus of as many as 25 Arabic dialects, plus Modern Standard Arabic (MSA). This means that we could extend our work, using the multi-task system to produce TArC corpora twins (for example in Algerian, Libyan and Moroccan).

These issues will certainly be the subject of future research oriented towards extension of the TArC. In fact, as noted by Professor Houda Bouamor, whom we thank for the suggestion, by extending our corpus (to other dialects and by collecting the spontaneous orthography in Arabic script), it could be useful for tasks such as automatic translit-eration into CODA or ADI itself. We could also work on a translation of the data into English, in order to extend the pool of use and association with other tools. However, a previous work we would concentrate our effort on, following the advice of Professor Agnès Tutin, whom we thank for it, concerns the annotation of foreign terms, both in terms of language classification and in terms of text lemmatisation and annotation in Part-of-Speech. Regarding the latter we are considering adding an annotation level according to the Universal Dependencies formalism for all the texts collected in TArC.

In fact, the annotation of the foreign terms will allow us to perform deeper analyses of the code-switching phenomena in Tunisian Arabic.

We have already reiterated many times that our work is 'hybrid'. This is because we consider it to be a strong point of our research. The fact that we could integrate the corpus building procedure with NLP procedures means that the corpus can then be used in NLP research, as well as linguistic research. The possibilities for extending the work presented here are varied in both fields and this is mainly because the TArC stands in a research context which is still rather unknown. In fact, our motivations for building a corpus from scratch, beyond leading us to produce tools ready for our research, met motivations related to the utility we imagined it could have upon the release of the tools created. Therefore, this utility not only concerns our future work, but also that of the scientific community as a whole, both from a linguistic and a NLP perspective. The same script can be used to observe the det's assimilation analyses. In order to obtain the data of table 4.12 and 4.14, it is possible to compile a txt file containing all the DPs and PPs which presents a N starting with a coronal consonant.

Bibliography

The txt file is structured as shown in the following example: The txt file has been manually checked to observed if the arabizi word was representing the det's assimiltion. If the assimilation has been represented in arbish, the phrase has been markes with _yes, under the voice analysis, if assimilation has not been represented in arabish, the phrase has been marked with _no, if the determiner is absent in arabish, the phrase has been marked with _abs. The analyzed file is available on the reference website as 'annotated_coronals.tsv'

Whith the annotated file the DPs and PPs where organized in the different cathegories in order to make the percentages of the table 4.12 and 4.14. Last information of this query are:

'The Pearson's chi-square test between assimilation rendering and participation of a PP presents a p_value of (0.0) being thus significative.

'The Pearson coefficient between assimilation (rendered or not) and the space before the N presents a p_value (0.0) which is significative

In the end with the same script we perforemd the diachronic and diastratic analyses of DPs with and without assimilation, with the relative Pearson's chi-square and pvalues.

  Rural speeches, of the countryside. 2. Hilali, Bedouin (Qarab), from the middle of the 11th century. (a) Hilali Arabic, from Syria. (b) Sulaymi Arabic, from Arabia. (c) MaQqili Arabic, from Yemen. (Taine-Cheikh, 2017) 21

  taken seriously. Nevertheless, 'defenders of the purity of CA' have always perceived TA as a permanent threat to the high variety of Arabic because of its vitality. As Daoud mentioned back in 2011, when CMC was still in its infancy in Tunisia, the fears of the 'defenders of the purity of CA' were further exacerbated due to the use of TA for chat and text message communications, which did not even use the Arabic alphabet, but what Daoud refers to as the French alphabet, explaining that the younger Tunisian generations 'resort to numbers to represent the distinctive (guttural) Arabic sounds'.

'

  Sie unterscheidet sich von der allgemeinen stadttunisischen Koine der Gesamtregion in den Ohren der nicht zu ihr gehörenden Bevölkerungsgruppen durch eine gewisse preziöse Artikulationsart (z. B. starke Imäla zuweilen auch bei Männern, die gelegentlich desgleichen die in der Frauensprache erhaltenen Diphtonge artikulieren) und eine ganze Anzahl ihr eigener Vokabeln, die außerhalb dieser engsten Gruppe nicht verwendet werden.' 42

Figure 1 . 1 :

 11 Figure 1.1: Association Derja Presentation

  noted: 'Dans la conversation orale, les personnes s'entendent, perçoivent timbre et intonation, souvent aussi gestes, mimiques et déplacements. Ils peuvent réagir rapidement, interrompre l'interlocuteur, intégrer ou refuser d'autres interlocuteurs, etc. Dans la communication par SMS ou WhatsApp tout cela est bien différent. Parmi les caractéristiques les plus saillantes de la communication médiée par téléphone portable, on compte le recours au code écrit, le régime temporel différé à échéance variable et une disposition spatiale où l'absence physique de l'autre est dominante.' 67

  been created to facilitate online Arabic writing, such as Yamli 77 or Microsoft Maren,78 which are smart Latin character keyboard input systems for Arabic, that allow for the conversion of text into Arabic characters through the user's selection of the desired word from a range of possibilities listed in a drop-down menu. Today it is possible to install the Arabic keyboard on any 'smart' device. However, there is still a large number of Tunisian social network users who still prefer Arabizi. The most likely reason for this is that Arabizi is easier, faster and more flexible than Arabic encoding, but also because it is considered cool and stylish by young CMC users in Saudi Arabia, according to interviews by[START_REF] Alghamdi | Arabizi in saudi arabia: A deviant form of language or simply a form of expression[END_REF] during their sociological survey on Arabizi beliefs. 79 Another reason is the already acquired familiarity with the Latin keyboard, as Bou Tanios (2016) reports with regard to the Lebanese context. Despite Facebook's meteoric rise in popularity since 2006, and the arrival of Twitter in the same year, the Arabic version of Facebook did not arrive until 2009, with the Twitter version being completed in 2012[START_REF] Alghamdi | Arabizi in saudi arabia: A deviant form of language or simply a form of expression[END_REF]. As shown in[START_REF] Salem | Social media and the internet of things towards data-driven policymaking in the arab world: potential, limits and concerns[END_REF], Facebook has had a huge impact on Tunisian society in comparison to the rest of the social networks, such as Twitter, Linkedin and Google+. Tunisia is, in fact, the third most active Arabic country on Facebook, considering it a daily activity. Twitter is less widely used, reaching only 2%.80

  85 Tunisian Neo-Arabic is characterised by a spontaneous Pimāla of medium intensity. There are in fact different degrees of /a/ palatalisation in Tunisian. Depending on the phonological context, the speaker provenience and also their gender, Pimāla can be light (h ˇafīfa, towards [ae]), medium (mutawassit . a, such as [e] in the ending of monosyllabic words as /smā/, ["sme:], 'sky', or [E] as in /bāb/, ["bE:b], 'door',

⋆

  Outsider late system morphemes, which are activated to indicate relationships within the clause, outside of its immediate constituent. The example features the agreement of the verb r . āpa, from the French rapper, with the first person singular subject. (18) Nrapi manich conscient f les chansons Njib zéro vues ama naj7 b mention, /nr . āpi ma-nī-š conscient fi les chansons nžīb zéro vues āma nāžaè bi mention/, 'I do rap without being aware of the songs, I get zero views, but I obtained a mention'.

  distinguish four different kinds of online written communication:

  be outlined in the next chapter, together with TArC's annotation semi-automatic processing.

  Committee for Research Ethics in the Social Sciences and the Humanities (NESH) of the Norwegian National Research Ethics Committees. 10 'Technological development is advancing rapidly, and this raises new challenges for research ethics. [...] Internet based social networks such as Facebook became widespread around 2005, and in all the sharing of information it is sometimes unclear what is public and what is private. Smartphones and mobile Internet connections (3G, 4G, Wifi) appeared around 2008, with various apps that register data on health and location, raising issues pertaining to data storage and surveillance. Furthermore, since 2010, development in the areas of digitalisation and automatisation has led to production, dissemination and storage of huge amounts of data at an increasing pace, often in real time. As a result of this rapid development, the criteria for what

  of the corpus in academic publications (such as papers and theses); 4. Publishing the corpus containing the blog with a non-commercial Creative Commons license (CC BY-NC or CC BY-NC-SA) in order to support researchers from the scientific community with their research on Tunisian dialects.' In the end, we opted for license Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) 11 .

  state that there were several early examples of work that could have been characterised as AI, but Alan Turing's vision was perhaps the most influential. Of particular note is his 1950 article 'Computing Machinery and Intelligence', in which he introduced the Turing test, on machine learning, genetic algorithms, and reinforcement learning. One of the most important figures in the study of biological and mechanical control systems and their connection to cognition was the mathematician Norbert Wiener. In the late 1940s, Wiener, along with Warren McCulloch, Walter Pitts, and John von Neumann, organised a series of influential conferences exploring new mathematical and computational models of cognition. Wiener's book 'Cybernetics' (1948) became a bestseller and awakened the public to the possibility of artificially intelligent machines. Finally, in 1958 Frank Rosenblatt proposed the 'perceptron' as an entity with an input layer and an output layer and a learning rule, based

  Figure 2.1: Feedforward Network.

Figure 2 . 2 :

 22 Figure 2.2: Recurrent Network unfolding graph.

Figure 2 . 4 :

 24 Figure 2.4: Encoder-decoder model.

Figure 2 . 5 :

 25 Figure 2.5: Attention mechanism system.

  , which produces morphological annotation and spelling corrections. MADARi was initially using MADAMIRA Egyptian, but was later extended with CALIMAGLF for better lexical matching. Alharbi et al. (2018) presented a POS tagger for the Arabic Gulf dialect, using a Bi-LSTM labeller, which showed better results in comparison with a Support Vector Machine (SVM) model. 21 In addition, Zalmout et al. (2018) presented a neural morphological tagging and disambiguation model for the Egyptian dialect. The system particularly relies on LSTM and CNN models for generating character embedding, with various extensions to handle noisy and inconsistent content. For the Moroccan and Sanaani Yemeni dialects, Al-Shargi et al. (2016) built a morphologycal analyser, which was trained on a morphologically annotated corpus, that the authors manually constructed exploiting the DIWAN annotation interface[START_REF] Al-Shargi | Diwan: A dialectal word annotation tool for Arabic[END_REF]. A general dialectal morphological analyser is the Analyser for Dialectal Arabic Morphology (ADAM), which[START_REF] Salloum | Adam: Analyzer for dialectal Arabic morphology[END_REF] present as comparable in its performances to CALIMA for Egyptian Arabic. Finally,[START_REF] Samih | A neural architecture for dialectal Arabic segmentation[END_REF] dealt with dialectal Arabic tokenisation with a neural architecture. The authors addressed the task as a sequence labelling problem at the character level, showing the importance of annotated data in developing ANLP models and that these models are good competitors to state-of-the-art methods.

  tures in order to support Egyptian-MSA identification. Starting with Egyptian Arabic,[START_REF] Bouamor | A Multidialectal Parallel Corpus of Arabic[END_REF] selected a set of 2000 sentences and translated them into four other Arabic dialects (Tunisian, Jordan, Palestinian and Syrian) to present the first multi-dialectal Arabic parallel corpus for DI. The original sentences in Egyptian were extracted by the Egyptian-English corpus built by[START_REF] Zbib | Machine translation of Arabic dialects[END_REF], who employed non-professional translators to perform the Egyptian-English translation on Mechanical Turk.[START_REF] Tachicart | Automatic identification of moroccan colloquial Arabic[END_REF] also presented a Moroccan dialect and MSA DI system. The authors relied on two different approaches: rule-based (relying on stopwords) and that based on statistics (using several machine learning classifiers). The statistical approach outperformed the rule-based approach, wherein the SVM classifier was more accurate than the other statistical classifiers. For the identification of the same varieties as in[START_REF] El-Haj | Arabic dialect identification in the context of bivalency and code-switching[END_REF],[START_REF] Ali | Character level convolutional neural network for Arabic dialect identification[END_REF] proposed a character-level convolutional neural network model working with dialect embedding vectors. As regards the latter,[START_REF] Shon | Convolutional neural networks and language embeddings for end-to-end dialect recognition[END_REF] extracted dialect embedding with an end-to-end dialect identification system and a Siamese neural network. 23 By exploiting the MADAR corpus[START_REF] Bouamor | The madar arabic dialect corpus and lexicon[END_REF], which will be presented below,[START_REF] Salameh | Fine-grained arabic dialect identification[END_REF] presented a fine-grained dialect classification covering 25 Arab-city dialects in addition to MSA.

  [START_REF] Erdmann | Addressing noise in multidialectal word embeddings[END_REF] addressed the problem of noise in multi-dialectal word embedding by collecting a multi-dialectal corpus by concatenating pieces from six different corpora: Almeman and Lee (2013); Khalifa et al. (2016a); Zbib et al. (2012); Bouamor et al. (2018); Zaidan and Callison-Burch (2011); Jarrar et al. (

  collected in these resources are not examples of spontaneous conversations. A version of MADAR encoded in CODA Star has recently been released. The MADAR CODA Corpus contains 10,000 sentences written in CODA, along with their original raw form.

  Besides the corpora, there have been a number of pieces of NLP work focused on building lexicons, such as[START_REF] Sghaier | Tunisian dialect-modern standard arabic bilingual lexicon[END_REF];[START_REF] Boujelbane | Génération des corpus en dialecte tunisien pour la modélisation de langage d'un système de reconnaissance[END_REF];Boujelbane et al. (2013a,b). In particular, the latter two describe the methods used for building a bilingual MSA-Tunisian lexicon by transforming PATB[START_REF] Maamouri | The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus[END_REF] into Tunisian through a rules-based system based on MSA-Tunisian relations.[START_REF] Boujelbane | De l'arabe standard vers l'arabe dialectal: projection de corpus et ressources linguistiques en vue du traitement automatique de l'oral dans les médias tunisiens[END_REF] projected an MSA corpus, and in particular its lemmas, to a Tunisian corpus by relying essentially on the bilingual MSA-Tunisian lexicon built byBoujelbane et al. (2013b);[START_REF] Boujelbane | Génération des corpus en dialecte tunisien pour la modélisation de langage d'un système de reconnaissance[END_REF].[START_REF] Hamdi | Automatically building a Tunsian lexicon for deverbal nouns[END_REF] built lexica (lexicon of verbs, of de-verbal nouns and of particles) with the aim of building a Part-of-Speech tagger for Tunisian. By contrast,Sadat et al. (2014b) instead built a Tunisian-MSA lexicon of 1,600 Tunisian words manually translated into MSA, in order to develop a rulebased translation system. Specific ontologies were then created to support dialogue systems for certain contexts, such as those inGraja et al. (2011aGraja et al. ( ,b, 2015)),Karoui et al. (2013a,b), based on the TuDiCoI corpus[START_REF] Graja | Lexical study of a spoken dialogue corpus in tunisian dialect[END_REF] again referred to in table 2.11. Among the Tunisian ontologies already mentioned, AebWordNet[START_REF] Karmani | Construction d'un wordnet standard pour l'arabe tunisien[END_REF][START_REF] Karmani | Building a standardized wordnet in the iso lmf for aeb language[END_REF][START_REF] Moussa | Tunisian Arabic aeb wordnet: Current state and future extensions[END_REF] also developed starting from the English-Tunisian Peace Corps Dictionary(Abdelkader, 1977). Section 2.5.2 provided an insight into how many studies have been carried out for the Dialect Identification task. The work that also includes Tunisian can be found in[START_REF] Belgacem | Automatic identification of Arabic dialects[END_REF],Lachachi and Adla (2016a,b, 2015),[START_REF] Neifar | Implementation of a symbolic method for the Tunsian dialect understanding[END_REF]), Sadat et al. (2014a),[START_REF] Hassine | Maghrebian dialect recognition based on support vector machines and neural network classifiers[END_REF] and[START_REF] Masmoudi | Conditional random fields for the Tunsian dialect grapheme-tophoneme conversion[END_REF][START_REF] Masmoudi | Automatic speech recognition system for Tunsian dialect[END_REF]. Concerning the morpho-syntactic analysis, a rule-based morphological analyser has been presented by[START_REF] Mcneil | Tunisian Arabic morphological parser[END_REF] and[START_REF] Karmani | Construction d'un wordnet standard pour l'arabe tunisien[END_REF]. On the other hand,[START_REF] Zribi | Sentence boundary detection for transcribed Tunisian Arabic[END_REF] focused on sentence boundary detection of Tunisian transcriptions by employing three different methodologies, i.e. one based on linguistic rules, another with a statistical approach, and the latter being a hybrid approach based on the previous two.Zribi et al. (2013b) adapted the MSA morphological analyser Al-khalil[START_REF] Boudlal | Alkhalil morpho sys1: A morphosyntactic analysis system for arabic texts[END_REF] for Tunisian, using a set of MSA verbs and nominal patterns transformed into Tunisian patterns, as well as the definition of a set of Tunisian affixes.[START_REF] Zribi | Morphological disambiguation of Tunsian dialect[END_REF] proposed a disambiguation method for Tunisian morphological analysis. As has already been made clear,[START_REF] Hamdi | Pos-tagging of Tunsian dialect using standard Arabic resources and tools[END_REF] exploited the relationship between MSA and Tunisian in order to build a POS-tagger for the latter, while[START_REF] Mekki | Syntactic Analysis of the Tunisian Arabic[END_REF] focused on Tunisian Treebank building.

  work of van der Wees et al. (2016), who implemented an Arabizi-Arabic transliteration pipeline based on Arabizi character mapping to Arabic sequences with the aim of improving Arabizi-to-English Statistical Machine Translation (SMT). With regards to the relationship between translation and transliteration, Guellil et al. (2017a,b) presented a system for translating between Algerian Arabizi and MSA. The authors proposed a comparison between statistical and neural translations after the translation step, show-ing that the quality of transliteration directly affects the translation. In another work, namely[START_REF] Guellil | Sentialg: Automated corpus annotation for algerian sentiment analysis[END_REF], in order to process Arabizi, the transliteration task has been shown to be a necessary previous step in order to reduce Arabizi ambiguity, which is a consequence of its lack of spelling conventions. In that particular study, the main task was sentiment classification of an Algerian Arabizi corpus. It appears that most of the work has been focused on automatic transliteration from Arabizi into the Arabic script, such as in[START_REF] Chalabi | Romanized arabic transliteration[END_REF],[START_REF] Darwish | Arabizi detection and conversion to Arabic[END_REF] and Al-Badrashiny et al.

2.

  Age range: [-25], [25-35], [35-50], [50+].

  (lit.: With frankness).

5.

  Elements classified as foreign or emotag, or POS-tagged as PUNC, NOUN_NUM

3 . 4 .

 34 Decoder ar for CODAfication, Decoder tk for Tokenisation, 5. Decoder pos for POS-tagging.

4

 4 and L 5 ) is than summed to obtain the global loss of the model (L) and jointly learn all the tasks (L = 5 i=1 L i (o i , ôi )). The sum is represented with the circled + symbol at the upper part of the figure3

Figure 3 . 1 :

 31 Figure 3.1: A high-level schema of our architecture

  can also be written in a textual file or observed in the Python shell. The following example (24) shows an extract of the textual file written in order to observe the data considered within the code-switching analyses outlined in section4.3.2. 2 The sentences reported in the example correspond to the first code-mixed NP sentence and the fifth NP sentence, which is also the first part for making a code-mixed PP. The output file reports the information about the selected code-mixed NPs, such as El farine in the first sentence, and the PPs, such as in the case of lel famille in the second sentence. The information given includes the corresponding POS tags and indexes in TArC, together with the whole sentence in Arabizi, the textual genre to which it belongs (social ), and the users' information (origin, age and gender ), if collected. (24) 1. <Mixed NP: [El farine]> <POS: DET_foreign> <TArC idx: 830-831> <Within sentence: t7out El farine w mele7 w tsoub 3lihom 50 g zebda dhayba w t5alet> 3 <Genre: social> <Users' metadata:> <origin:/> <age:35-50> <gender:F> 2

  combinations, our hypothesis is that, in the case of a Prepositional Phrase (PP) that includes the article (det), the combinations are arranged along a continuum from the most representative of Tunisian orality ([prep+det n]) to the most representative of the orthographic system of Standard Arabic ([prep det+n]).

Figure 4 . 1 :

 41 Figure 4.1: NP with assimilated Det. The N's first consonant is a coronal.

Figure 4 . 2 :

 42 Figure 4.2: NP with not-assimilated Det. The N's first consonant is a coronal.

Figure 4 . 3 :

 43 Figure 4.3: Assimilation through gender. Gr stands for 'Graphically rendered'.

Figure 4 . 4 :

 44 Figure 4.4: Assimilation through age range. GR stands for 'Graphically rendered'.

'

  ), which are included below. In fact, the three examples concern the Nominal Phrase in Tunisian Arabizi and present different encoding realisations of the determiner followed by a foreign noun. The example (16) presents an intrasentential code-switched defined NP, where the determiner (el ) corresponds to the Tunisian morpheme, while the N (famille) belongs to the French lexicon. Instead, in example (17), the whole NP (une zone) is made of French elements.19 (16) hatta el famille walit j evite tout le monde, Also the family I started to avoid everybody'.

( 18 )

 18 Nrapi manich conscient f les chansons Njib zéro vues ama naj7 b mention, /nr . āpi ma-nī-š conscient fi prep T rap without being aware of the songs I get zero views but I obtained a mention'.The first consideration for example (18) is about the prepositions being employed in the PPs. As observed in section 4.2.1, the /fi / preposition tends to occur in each type of PP scheme, without any restrictions. 20 This is not the same as for the preposition occurring in the second PP (/b-/), for which a tendency to merge with the noun has been witnessed.21 It can also be seen, in table 4.2, that the preposition /b-/ rarely appears in schemes in which the preposition is separated by the det of the N, such as [prep det+n] and [prep det n]. The results shown in tables 4.3 and 4.2 have raised the issue of the preposition playing a role in selecting the orthography realisation of

were only 3

 3 occurrences (namely 2.4% of all the sentences and 4.1% of the 76 PPs) in which the preposition appears to be separated from the determiner([prep det n] or [prep det+n]). As reported in the file generated by the query script, these three sentences are: (28) 50.PREP(33) <Mixed NP: [mte3 el GEOGRAPHIE]> <POS: PREP_DET_foreign> <TArC idx: 26419-26420> <Within sentence: chouf ya mte3 el GEOGRAPHIE el msetria wel mhedwia aktharhom mkach5in et je crois elli ennes el kol ta3ref hetha wé n7eb nkollek 7aja rahou wled el 3asma 9afzin 3lina barcha felli t7eb inti ma3ada ettourisme > <Genre: forum> <Users' metadata:> <origin:/> <age:/> <gender:/> 22 (29) 93.PREP(56) <Mixed NP: [m3a l commandant]> <POS: PREP_DET_foreign> <TArC idx: 36682-36683> <Within sentence: fi salla mta3 l'équipage m3a l commandant bidou , expérience wehed may3ichHech martine > <Genre: blog> <Users' metadata:> <origin: Tunis> <age: 25-35> <gender: F> 23 (30) 99.PREP(61) <Mixed NP: [ala l récup']> <POS: PREP_DET_foreign> <TArC idx: 37358-37359> <Within sentence: 3jebni fel galerie hethi li heyya feha barcha des oeuvres basées essentiellement ala l récup' wel recyclage ; Mathalan , blalet ma3moulin b mgharfa mta3 glaces , plastique ethika elli na7na nlaw7ouha , tableau yfatte9 ma3moul ala tarf hdid m5azza , prtrait afro w fazet , ala beb lou7 mdagdeg ! > <Genre: blog> <Users' metadata:> <origin: Tunis> <age: 25-35> <gender: F> 24 22 My own translation: 'Look, (you who are) expert in geography, the people of Monastir and the people of Mahdia, most of them, are Taraji fans and I think everyone knows this thing. And I want to tell you something, right them, the children of the capital (they) are much smarter than us in everything you want, except tourism'.

Figure 4 . 5 :

 45 Figure 4.5: Percentage of male and female users realising /q/ as [q] outside Tunis.

Figure 4 . 6 :

 46 Figure 4.6: Percentage of male and female users realising /q/ as [g] outside Tunis.

Figure 4 . 7 :

 47 Figure 4.7: Percentage of male users realising /q/ as [q] and [g] in Tunis.

Figure 4 . 8 :

 48 Figure 4.8: Percentage of female users realising /q/ as [q] and [g] in Tunis.

  These results invite us to at least leave open the question of the convergence of the Tunisian varieties on the model of Tunis (table 4.30), which happens to present percentages of the distribution of diphthongs (preserved or reduced) which are very similar (if not almost identical) to those found for the sum of all the other governorates (excluding that of Tunis, table

Figure 4 . 9 :

 49 Figure 4.9: Plural realisation outside Tunis.Figure 4.10: Plural realisation in Tunis.

Figure 4 .

 4 Figure 4.9: Plural realisation outside Tunis.Figure 4.10: Plural realisation in Tunis.

Figure 4 .

 4 Figure 4.11: Social Net.Figure 4.12: Forum Figure 4.13: Blog

Figure 4 .

 4 Figure 4.11: Social Net.Figure 4.12: Forum Figure 4.13: Blog

  Abainia, Kheireddine; Ouamour, Siham, and Sayoud, Halim. A novel robust Arabic light stemmer. Journal of Experimental & Theoretical Artificial Intelligence, 29(3): 557-573, 2017. Abd-El-Jawad, Hassan R. Cross-dialectal variation in Arabic: Competing prestigious forms. Language in Society, pages 359-367, 1987. Abdelali, Ahmed; Darwish, Kareem; Durrani, Nadir, and Mubarak, Hamdy. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations, pages 11-16, 2016. Abdelali, Ahmed; Mubarak, Hamdy; Samih, Younes; Hassan, Sabit, and Darwish, Kareem. Arabic dialect identification in the wild. arXiv preprint arXiv:2005.06557, 2020. Abdelkader, Ben. Peace Corps English-Tunisian Arabic Dictionary. ERIC Cleringhouse, Washington D.C., 1977. Abdul-Mageed, Muhammad and Diab, Mona. Awatif: A multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In Proceedings of the 8th Language Resources and Evaluation Conference (LREC), volume 515, pages 3907-3914, 2012. Abdul-Mageed, Muhammad and Diab, Mona. Sana: A large scale multi-genre, multidialect lexicon for arabic subjectivity and sentiment analysis. In Proceedings of the 9th Language Resources and Evaluation Conference (LREC), pages 1162-1169, 2014. Abdul-Mageed, Muhammad; Alhuzali, Hassan, and Elaraby, Mohamed. You tweet what you speak: A city-level dataset of arabic dialects. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC), 2018. Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim, and Ungar, Lyle. Toward micro-dialect identification in diaglossic and code-switched environments. arXiv preprint arXiv:2010.04900, 2020. Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim; Bouamor, Houda, and Habash, Nizar. Nadi 2021: The second nuanced arabic dialect identification shared task. arXiv preprint arXiv:2103.08466, 2021. Abidi, Karima and Smaïli, Kamel. An automatic learning of an algerian dialect lexicon by using multilingual word embeddings. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC), 2018. Abidi, Karima; Menacer, Mohamed Amine, and Smaili, Kamel. Calyou: A comparable spoken algerian corpus harvested from youtube. In 18th Annual Conference of the International Communication Association (Interspeech), 2017. Adouane, Wafia; Semmar, Nasredine; Johansson, Richard, and Bobicev, Victoria. Automatic detection of arabicized berber and arabic varieties. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 63-72, 2016. Akbar, Rahima. Arabizi among kuwaiti youths: Reshaping the standard Arabic orthography. International Journal of English Linguistics, 9(1):301-323, 2019. Al-Badrashiny, Mohamed; Eskander, Ramy; Habash, Nizar, and Rambow, Owen. Automatic transliteration of romanized dialectal Arabic. In Proceedings of the eighteenth conference on computational natural language learning, pages 30-38, 2014. Al-Sabbagh, Rania and Girju, Roxana. Yadac: Yet another dialectal Arabic corpus. In Proceedings of the 8th Language Resources and Evaluation Conference (LREC), pages 2882-2889, 2012. Al Sallab, Ahmad; Hajj, Hazem; Badaro, Gilbert; Baly, Ramy; El-Hajj, Wassim, and Shaban, Khaled. Deep learning models for sentiment analysis in Arabic. In Pro-Allal, Amin and Geisser, Vincent. Tunisie. Une démocratisation au-dessus de tout soupçon? Cnrs, 2018. Allehaiby, Wid H. Arabizi: An analysis of the romanization of the Arabic script from a sociolinguistic perspective. Arab World English Journal, 4(3), 2013. Almeman, Khalid and Lee, Mark. Automatic building of arabic multi dialect text corpora by bootstrapping dialect words. In 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), pages 1-6. IEEE, 2013. Alonso, Héctor Martínez and Plank, Barbara. When is multitask learning effective? semantic sequence prediction under varying data conditions. arXiv preprint arXiv:1612.02251, 2016. Alsarsour, Israa; Mohamed, Esraa; Suwaileh, Reem, and Elsayed, Tamer. Dart: A large dataset of dialectal arabic tweets. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC), 2018. Aly, Mohamed and Atiya, Amir. Labr: A large scale arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 494-498, 2013. Ameur, Hanen; Jamoussi, Salma, and Hamadou, Abdelmajid Ben. Exploiting emoticons to generate emotional dictionaries from facebook pages. In Intelligent Decision Technologies 2016, pages 39-49. Springer, 2016. Androutsopoulos, Jannis. Language change and digital media: a review of conceptions and evidence. Standard languages and language standards in a changing Europe, 1: 145-159, 2011. Androutsopoulos, Jannis. "Greeklish": Transliteration practice and discourse in the context of computer-mediated digraphia. Orthography as social action: Scripts, spelling, identity and power, pages 359-392, 2012. Androutsopoulos, Jannis and Schmidt, Gurly. Sms-kommunikation: Ethnografische gattungsanalyse am beispiel einer kleingruppe. Zeitschrift für angewandte Linguistik,36:49-80, 2002.

Figure A. 1 :

 1 Figure A.1: Diachronic analyses of DPs with and without assimilation.

Figure A. 2 :

 2 Figure A.2: Diastratic analyses of DPs with and without assimilation.

Table 1 :

 1 Table of Transcription and Transliteration consonant system used.

	Long Vowel Tr. Used Grapheme Name Graphemes Short Vowel Tr. Used
	ā	Palif	@

Table 1

 1

.1: Distinctive phonological features of sedentary and Bedouin varieties.

  ).

		OA features	sedentary v.	Bedouin v.
	1.	2pl. ̸ = 3pl. (verbs, pron.)	2pl. = 3pl.	2pl. ̸ = 3pl.
	2.	3sg. pron. suff. */-hu/	/-u/	/-ah ∼ -ih/
	3.	verbal time-aspect	innovative	conservative
	4.	passive verb (ablaut in CA)	innovative	conservative
	5.	dual forms	less maintained more maintained
	6.	genitive phrase	analytical	synthetical
	7. agreement with pl. heads[-human] not maintained	maintained
	8.	gender and number agreement	not complete	complete

Table 1.2: Distinctive morphological features of sedentary and Bedouin varieties.

  The sociologist ante litteram Ibn Khaldūn (Tūnis 1332 -al-Qāhira 1406) is best known for the introduction (muqaddima, 1377) to his universal history essay kitābu l-Qibar (lit.'book of examples').23 This introduction is an essay on social history, in which Ibn Khaldūn discusses the origins and development of civilisation, taking the dichotomy between Bedouin and sedentary life as a starting point. According to his view of history, civilisation gradually developed from a Bedouin way of life towards a sedentary one. Furthermore, Ibn Khaldūn includes, in his outline of the development of civilisation, a discussion regarding the origins of the discipline of linguistics. He says that

	1.1.3 Diglossia & Multilingualism

  S.P.E.A.K.I.N.G. taxonomy. 2 According to Herring, the most important medium factors are:

	M1 -Synchronicity;	M2 -Message transmission (1-way vs. 2-way);
	M3 -Persistence of transcript;	M4 -Size of message buffer;
	M5 -Channels of communication; M6 -Anonymous messaging;
	M7 -Private messaging;	M8 -Filtering;
	M9 -Quoting;	M10 -Message format.

Table 2

 2

	Regarding the M2 parameter, Herring (2007) explains that it depends on the gran-
	ularity of the units that are transmitted through the technological device, character-
	by-character (2-way) or word-by-word, or even line-by-line (1-way), considering the
	implication that these different modalities have on simultaneous feedback availability
	during conversations. With respect to the M10 parameter, the scholar states that the
	order in which messages appear, or are organised, in the conversation structure, is
	not always chronological. He also affirms that this can influence the communicative
	practices on the platform. Herring (2007) also observed social and situational factors,
	which are shown in table 2.2.
	S1 -Participation structure; S2 -Participant characteristics;
	S3 -Purpose;	S4 -Topic or Theme;
	S5 -Tone;	S6 -Activity;
	S7 -Norms;	S8 -Code.

.1: Herring's medium factors in computer-mediated discourse.

Table 2

 2

.2: Herring's situational factors in computer-mediated discourse.

Table 2 .

 2

3: DNW characteristics.

Table 2 .

 2

4: Corpus Structural Criteria.

Table 2

 2

.5: Corpus Building Strategy.

Table 2 .

 2 6: TArC Structural Criteria.

Table 2 .

 2

7: TArC Metadata.

Table 2

 2

.8: MSA morpho-syntactic work.

  ).19 A different form of annotation concerns resources built for Sentiment Analysis (SA), such as the Large Scale Arabic Sentiment and Emotion Lexicon (ArSEL)[START_REF] Badaro | Arsel: A large scale arabic sentiment and emotion lexicon[END_REF]

	the Sentiment Lexicon for Standard Arabic (SLSA) (Eskander and Rambow, 2015),
	the Opinion Corpus for Arabic (OCA) (Rushdi-Saleh et al., 2011), the Multi-Genre
	Corpus for MSA Subjectivity and Sentiment Analysis (AWATIF) (Abdul-Mageed and
	Diab, 2012), and the Large-scale Arabic Book Review data set (LABR) (Aly and Atiya,

Table 2 .

 2 9: Dialectal-Arabic corpora before 2016.

	Corpus References	Brief Description
	AraSenTi-Tweet	17,573 Saudi tweets semi-automatically annotated into four classes:
	(Al-Twairesh et al., 2017)	positive, negative, neutral and mixed.
	Curras	56,000 tokens in Palestinian Arabic, annotated using MADAMIRA and
	(Jarrar et al., 2017)	DIWAN tools (Pasha et al., 2014; Al-Shargi and Rambow, 2015).
	no-name	Arabic-Egyptian comparable Wikipedia corpus, that contains
	(Saad and Alijla, 2017)	10,197 aligned documents.
	CALYOU	12.7 million Algerian YouTube comments, aligned using Word2Vec
	(Abidi et al., 2017)	(Mikolov et al., 2013).
	SentiAlg	Algerian sentiment corpus based on an Algerian sentiment lexicon
	(Guellil et al., 2018)	automatically built by translating two English lexicons (SOCAL and
		SentiWordNet (Taboada et al., 2011; Esuli and Sebastiani, 2006))
		using Glosbe API (Guellil and Faical, 2017). It contains Arabizi.
	No-name	1/4 billion tweets from 29 major Arab cities.
	QADI	Automatically collected dataset of 540,000 tweets from 2,525 users
	(Abdelali et al., 2020)	across 18 Arab countries.

(Abdul-Mageed et al., 2018)

Data is tagged at city level. Table 2.10: Most recent Dialectal-Arabic corpora.

Table 2

 2

	.11: Tunisian Neo-Arabic corpora, excluding Tunisian Arabizi corpora

  social networks, written pieces of theatre, dictionaries,

		transcriptions of spontaneous speech, etc.
	LETD contact	Electronic Tunisian Dialect: 43,222 messages in Latin
	(Younes and Souissi, 2014) script exclusively constructed from the web.
	(Masmoudi et al., 2015)	Social media and SMS corpus (870,904 words)
		for CODA transliteration.
	TLD & TAD contact	420,897 & 160418 words in a Latin & Arabic script raw
	(Younes et al., 2015)	corpora, automatically constructed from the web.
	Sentiment Analysis corpus Comments from the Facebook pages encoded in Arabic
	(Ameur et al., 2016)	and Latin scripts. Annotated for SA with 9 different tags.
	TSAC, downloadable	Tunisian Sentiment Analysis Corpus. 17,060 Tunisian

[START_REF] Mdhaffar | Sentiment analysis of tunisian dialects: Linguistic ressources and experiments[END_REF]

Facebook comments, in Arabic and Latin script.

Table 3 . 1 :

 31 Example of the fifteen thematic categories 5. Get data, once selection has concluded.

Table 3 .

 3

	Verbs	Nominals	Particles	Other
	VERB	NOUN	PREP	PUNC
	PSEUDO_VERB	NOUN_NUM	CONJ	ABBREV
	PV	NOUN_QUANT	SUB_CONJ	INTERJ
	PV_PASS	NOUN.VN	PART	LATIN
	PVSUFF_DO:<PGN>	NOUN_PROP	CONNEC_PART	FOREIGN
	PVSUFF_SUBJ:<PGN>	ADJ	EMPHATIC_PART	TYPO
	IV	ADJ_COMP	FOCUS_PART	PARTIAL
	IV_PASS	ADJ_NUM	FUT_PART	DIALECT
	IVSUFF_DO:<PGN>	ADJ.VN	INTERROG_PART
	IV<PGN>	ADJ_PROP	JUS_PART
	_MOOD:<mood>	ADV	NEG_PART
	CV	REL_ADV	RC_PART
	CVSUFF_DO:<PGN>	INTERROG_ADV	RESTRIC_PART
	CVSUFF_SUBJ:<PGN>	PRON	VERB_PART
		PRON_<PGN>	VOC_PART
		POSS_PRON_<PGN>
		DEM_PRON_<GN>
		REL_PRON
		INTERROG_PRON
		NSUFF<Gen><Num><Cas>
		CASE<Def><Cas>
		DET

2: The Buckwalter Tag Set

table , '

 , Train. Tokens' represents the number of tokens used to train the model at each step (the number of tokens from TArC are reported in parentheses, while the rest are tokens from the MADAR corpus).

		Train. tokens		LSTM
	Task		Class CODA* Token	PoS
		Corpus: MADAR
	Step0	12,391	99.83%	-	88.83% 72.71%
	Step0 complete	12,391	99.58% 76.77% 74.83% 67.59%
		Corpus: MADAR+TArC
	Step1	17,261 (4,870) 92.69%	-	77.66% 59.56%
	Step2	22,173 (9,780) 97.21%	-	87.53% 74.30%
	Step3	27,270 (14,870) 96.69%	-	91.47% 76.38%
		Corpus: TArC
	Step4	22,150	96.83% 75.30% 73.38% 69.76%
	Step5	27,435	97.17% 75.08% 73.07% 66.24%
	Step4 smart-init	22,150	95.91% 76.55% 74.96% 72.57%
	Step5 smart-init	27,435	97.08% 77.83% 75.69% 69.76%
		Corpus: MADAR Arabizi +TArC
	Step4	34,541 (22,150) 96.59% 78.94% 77.38% 74.54%
	Step6	46,197 (33,806) 97.63% 83.29% 81.94% 81.02%
	Step6 Arabic	46,197 (33,806) 92.78% 74.72%	-	-
	Step6 Token	46,197 (33,806)	-	-	89.54%	-
	Step6 POS	46,197 (33,806)	-	-	-	75.48%
	Final Step CODA* input	-	98.67%	-	96.78% 86.31%

Table 3 . 4 :

 34 Summary of results in terms of accuracy.

Table 3 .

 3 5: Some quantitative data of TArCAs seen in section 2.3, metadata is fundamental information for text data. As shown in table 2.7, the metadata collected in TArC is regarding text genre, publication date, and user information, such as gender, age range and governorate of belonging. It is believed that the information collected could assist with different kind of linguistic

	Total	4,797	43,327
		arabizi foreign emotag
	forum	755 6,024	5,873	12
	social	3,162 11,835	3,623	598
	blog	366 5,970	694	7
	rap	514 7,681	1,009	1

Table 3 .

 3 10: Percentage of tokens per year in TArC.

	tot: 43,327
	Years	%	Years	%	Years	%	Years	%
	2005	4.52%	2010	0.91%	2015	5.72%	2020	4.19%
	2006 :	3.82% 2011 : 7.2%	2016 : 5.91%
	2007 :	1.42% 2012 : 5.47% 2017 : 3.21%
	2008 :	0.7%	2013 : 4.63% 2018 : 14.16%
	2009 :	2.9%	2014 : 9.07% 2019 : 26.2%
		tot: 22,213			tot: 25,157
		Age ranges	%		Gender	%
		-25 :		14.23%	Male	62.13%
		25-35 :	74.19%	Female :	37.87%
		35-50 :	9.6%
		50+ :		1.97%

Table 3 .

 3 11: Percentage of TArC tokens for which age range and gender has been registered.

Table 4 . 2 :

 42 Distribution of the two chosen prepositions following different schemes. N.o. stands for 'no occurrences'

Upon examining table

(4.

2), it is evident that, in the case of prepositional phrases where the preposition does not attach to the NP, namely, the third and the fourth entries ([prep det+n] and [prep det n]), the preposition never appears ([prep det+n]) or appears very rarely (2.7% in

[prep det n]

Table 4 . 3 :

 43 The three most frequent prepositions through different schemes.

	As can be seen from table 4.3, the most commonly recurring prepositions in the
	different orthographic schemes are:
	1. /mtāQ/	, /Qala/	and /min/	for the schemes [prep det n], [prep det+n]
	and [prep n];
	2. /b-/ , /l-/	and /Qa/	are instead the most frequent for [prep+det+n],
	[prep+det n] and [prep+n];
	3. /fi/	is highly frequent in all compounds, so we can say that it has a super
	partes status.

Table 4 . 4 :

 44 Distribution of PrepositionalPhrase in case of not-articulated nouns.

Table 4 .

 4 5: Occurrences of arithmographemes in TArC.In addition, in order to clearly understand the mentioned table(table 4.8), it should be explained that the percentages are based on the exact number of tokens considered Therefore, in table 4.8, the relative frequency for each two-year subcorpus is shown so as to observe whether, at the quantitative level, these graphemes undergo changes over time.

	Arithmogr. Occurrences Arithmogr. Occurrences
	2	69	6	1
	3	2,108	7	1,463
	4	13	8	72
	5	598	9	1,125
	On the other hand, the use of 2, 4, 6 and 8, for , ,	and , is less consistent,
	as shown in table 4.8. However, when observing the distribution of these arithmo-
	graphemes over time, it is possible to affirm that 2, 4, 6 and 8 were not widely used
	since the beginning of Tunisian-Arabizi diffusion. It seems as though the most used
	arithmographemes in Arabizi (3, 5, 7 and 9 ) have also tended to follow the same
	distribution pattern as when this use first emerged.

Table 4 . 8 :

 48 Distribution of the use of arithmographemes through time in TArC. N.o. stands for 'no occurrences'.

  table 4.11. The purpose of this is to see whether prepositional phrase realisation in Tunisian Arabizi has varied over time, until we reach the distribution observed in the previous section (table4.1), or whether this distribution also reflects that of the early stages of Arabizi diffusion. The following table presents the percentage of each scheme occurrence in a specific year, divided into the total number of tokens of the same year (shown in table 4.6). In the above-mentioned table,

	prepositional schemes are organised into the following types:
	1. Type0 : [prep+n]
	2. Type1 : [prep n]
	3. Type2 : [prep+det+n]
	4. Type3 : [prep+det n]
	5. Type4 : [prep det+n]
	6. Type5 : [prep det n]

Table 4 .

 4 Group of Y. Tot of tokens Group of Y. Tot of tokens 9: Groups of years and respective amount of tokens.

	2005-10	6,496	2011-14	10,576
	2015-17	6,023	2018-19	10,770
	2019-20	8,357

Table 4 .

 4 10: Total number of PP occurrences per group of years.

		Group of Y. PP tot Group of Y. PP tot
		2005-10	156	2011-14	436
		2015-17	267	2018-19	257
		2019-20	215
	PP schemes 2005-2010 2011-14 2015-17 2018-19 2019-20
	Type0	10.26%	5.05%	21.35%	16.34%	15.35%
	Type1	45.51%	64.45%	50.94%	55.25%	51.16%
	Type2	12.18%	4.59%	8.99%	9.34%	11.16%
	Type3	22.44%	22.02%	16.48%	14.79%	17.67%
	Type4	4.49%	2.52%	2.25%	3.5%	3.26%
	Type5	5.13%	1.38%	n.o.	0.39%	0.93%
	TOT	100%	100%	100%	100%	100%

Table 4 .

 4 11: Distribution of PP usage through time in TArC. N.o. stands for 'no occurrences'

Table 4 .

 4 12: Distribution of PP usage through time in TArC.

	Total Nominal Phrases	1,336	Part of a PP	[det n]
	NP where N starts with coronal lett.	480
	NP where N does not start with coronal lett.	856
	NP where Det is not present	21
	NP where Det is assimilated	269	70	10 (3.7%)
	NP where Det is not assimilated	190	65	169 (88.9%)

Table 4 .

 4 13: Distribution of assimilated det through time in TArC.

	2019-20

Table 4 .

 4 14: Distribution of assimilated det through time in TArC. GR stands for 'Graphically Rendered'.

Table 4 .

 4 15: Distribution of det's assimilation through the age ranges. GR stands for 'Graphically Rendered'.

	Assimilation	-25	25-35 35-50 50+
	GR	77.8% 36.2% 80% 100%
	not GR	22.2% 63.8%	20%	n.o.
	Assimilation	M	F
	GR		24.7% 77.4%
	not GR	75.3% 22.6%
	Table 4.16: Distribution of det's assimilation through gender metadata. GR stands for
	'Graphically Rendered'.

Table 4 .

 4 3 highlighted the fact that /fi /preposition occurs with a high percentage in the following schemes: [prep+det n] (35%), but also in [prep det+n] (30%) and [prep det n] (33.3%).21 Table4.3 highlighted the fact that /b-/ tends to occur only within the PP schemes where it is joined to the noun (namely: [prep+n] (59%), [prep+det n] (25.4%) and [prep+det+n] (52%).

Table 4 .

 4 of prepositional phrases involving nominal elements that are the result of CS (tables 4.17), it appears that those making the most use of intrasentential code-switching are female forums users aged between 25 and 35 years, mostly coming from Tunis. Below is a table summarising four independent trends, which are plausibly correlated, but are not to be interpreted in this way. 26 17: Code-switched PPs in TArC. N.o. stands for 'no occurrences'.However, it must be remembered that, as shown in tables3.6, 3.7 and 3.8, data from Tunis and in the 25-35 age group are better represented in the corpus than the other governorates and age groups. Tunis represents 45.1% of the collected governorates for social networks, 19.73% of those for forums and 100% for blogs. The age range 25-35

	Govern.	%	Age	%	Gender	%	Text Genre	%
	Nabeul	2.63%	-25	6.52%	M	16.33%	Blog	26.5%
	Monastir	5.26%	25-35 86.96%	F	83.67%	Forum	51.3%
	Tataouine	2.63%	35-50	6.52%			Social N.	22.1%
	Tunis	89.47%	50+	n.o.
	represents 52.21% of the ages ranges collected for social networks users, 58.17% of those
	of on forums and 100% of the bloggers' ages. At the same time, however, the data on
	the gender of users remained quite balanced for all textual genres (again excluding
	rap, as a musical genre above all). Regarding the latter, social networks (with 16,056
	tokens) were better represented than forums (11,909 tokens) and blogs (6,671 tokens),
	but despite this, in table 4.17, the textual genre of forums stands out.
	Regarding the percentages reported in table 4.17 concerning gender, Pearson's chi-
	squared test was also performed (with a p-value < 0.01), which highlighted the relation-
	ship between gender and code-switching. In the following table (4.18), the frequencies
	used for the calculations in a contigency table are reported.

Table 4 .

 4 18: Contigency table of users' gender and CS data.Furthermore, regarding the textual genres, Pearson's chi-squared test show a dependence within data and the code-switching, with the p-value reaching 0.02. In table4.19, the frequencies of the data used in Pearson's chi-squared test are reported.

		Social N. Forum Blog Tot
	Switched	25	58	30	113
	Not Switched	173	129	106	549
	Tot	198	187	136

Table 4 .

 4 19: Contigency table of text genre and CS data.However, as shown in table4.20, by also testing PPs that do not include any codeswitching (237 sentences), the following percentages were obtained: 45.15% of the PPs were produced by female users and 54.85% were produced by male users.

		PPs without CS PPs with CS
	F	45.15%	83.67 %
	M	54.85%	16.33 %
	Tot	100%	100%

Table 4 .

 4 20: Comparison of percentages of PPs with or without CS according to users' gender.

Table 4 .

 4

	Governorates	bil-qala 81.03% bil-gala 18.96%
	Bizerte	19.79%	n.o.
	Ariana	7.29%	4.35%
	Zaghouan	2.08%	n.o.
	Nabeul	5.21%	n.o.
	Kairouan	4.17%	8.7%
	Sousse	19.79%	8.7%
	Monastir	6.25%	4.35%
	Mahdia	3.12%	n.o.
	Sfax	5.21%	8.7%
	Béja	1.04%	4.35%
	Gabès	3.12%	39.13%
	Jendouba	5.21%	4.35%
	Kebili	2.08%	n.o.
	El Kef	n.o.	4.35%
	Manouba	4.17%	n.o.
	Medenine	2.08%	4.35%
	Gafsa	2.08%	8.7%
	Sidi Bouzid	1.04%	n.o.
	Siliana	n.o.	n.o.
	Ben Arous	2.08%	n.o.
	Tataouine	2.08%	n.o.
	Tozeur	2.08%	n.o.
	Kasserine	n.o.	n.o.
	Tot	100%	100%

21: Diatopic analysis of /q/ realisation in Tunisia. N.o. stands for 'no occurrences'.

Table 4 .

 4 22: Diatopic analysis of /q/ realisation in Tunis.

Table 4 .

 4 23: Contingency table of /q/ realisation in Tunisia (except Tunis).

		Male Female Tot
	[q]	283	147	430
	[g]	104	118	222
	Tot	387	265
	Table 4.24: Contingency table of /q/ re-
	alisation all over Tunisia.

Table 4 .

 4 25: Diastratic analysis of /q/ realisation in Tunisia.

	Govs. except Tunis bil-qala bil-gala
	M		43.3%	90.3%
	F		56.7%	9.7%
	Tot		100%	100%
	Gov. of Tunis bil-qala bil-gala	Tot
	M	93.8%	6.2%	100%
	F	97.3%	2.7%	100%

Table 4 .

 4

26: Diastratic analysis of /q/ realisation in Tunis.

table 4 .

 4 30 As a result of these strategies, for example, the p-values in Pearson's chi-30, to see whether or not there is a convergence. Regarding table 4.28, only governorates for which occurrences have been found have been reported on. Regarding the quantity of occurrences found for data in table4.28, 26.73% of the total data presents diphthongs, while 73.27% of the total data presents diphthong reductions to a long vowel, as shown in4.29. 31

	squared test between governorates (excluding Tunis) and the etymological diphthong
	realisation is necessarily insignificant, being 0.98. Instead, when using the same test,
	between governorates (including Tunis) and the etymological diphthong realisation, the
	value is lower than 0.01, which makes sense when considering the greater amount of
	data we dispose of for the capital than for other specific governorates, and the clear
	tendency that marks Tunis data (table 4.30). In conclusion, what we can observe is a
	general trend by grouping together all the governorates excluding Tunis (as the model

of the koiné hypothesis), in table 4.28, and compare same to data from the capital, in

Table 4 .

 4 29: Diphthong realisation in Tunisia.

	Diphthongisation Monopht.
	26.2%	73.8%
	Table 4.30: Diphthong realisation in
	Tunis data.

Table 4 .

 4 32: Analysis of gender opposition in governorates except Tunis.

	Urban style Gender opposition
	98.48%	1.52%

Table 4 .

 4 33: Analysis of gender opposition in Tunis governorate.

Table 4 .

 4 34: Analysis of plural realisation in governorates except Tunis.

	Urban style Village and Bedouin style
	35.3%	64.7%
	Urban style Village and Bedouin style
	88.5%	11.5%

Table 4 .

 4

35: Analysis of plural realisation in Tunis governorate.

Table 4 .

 4

36: General Analysis of Text Genre.

Table 4 .

 4

	texts, such as blogs, but only occurs in
	around 4.7% on social networks and 7.7%
	in forums.
	With space	4.7%	7.7%	61.5%	26%

def.NP

Social Forum Blog Others 37: Distribution of space in NP through TArC genre. 'Others' consists of rap lyrics.

TArC and the query-tools are available at the following page: _urlhttps://github.com/eligugliotta/tarc.The architecture is available at the following link: https://gricad-gitlab.univ-grenoble-alpes.fr/dinarelm/tarc-multi-task-system.

For further discussion on topics surrounding linguistic ideology, see[START_REF] Woolard | Language ideology: Issues and approaches[END_REF].

Libyan-Berber is linked to the family of Afro-Asiatic languages.

The term barbarus, deriving from the greek βάρβαρος, was used by the Romans to name all non-Latin speaking populations, including Libyans.

There is evidence of a Sicilian settlement at Cape Bon and in the region of Kef, where two inscriptions written in an alphabet analogous to Etruscan writing were found near Tunis.

'The current dialect is, in fact, only the fruit of different strata (substrates, adstrates and superstrates): a beautiful syncretism that reflects an evolution of the Tunisian society through the different historical eras and that proportionally reflects the weight of the different cultures developed on this territory.' My own translation.

As previously stated, we refer here to the traditional classification while being aware that this narrative has recently been challenged (see[START_REF] Benkato | From medieval tribes to modern dialects: On the afterlives of colonial knowledge in arabic dialectology[END_REF]).

For further information about this new line of research see also[START_REF] Guerrero | The jbala-villageois dialects. a grammatical study of a rural typology of colloquial maghrebi arabic[END_REF] and[START_REF] Mion | Éléments de description de l'arabe parlé à mateur (tunisie)[END_REF].

1.1. Historical and Linguistic Introduction linguistic situation, starting from the relationship between Arabic, as the official language of Tunisia, and Tunisian Neo-Arabic, as the Tunisians' mother tongue.

The full title is: Kitābu l-Qibari wa dīwāni l-mubtadaP wa-l-h ˇabar fī ayyāmi l-QArab wa-l-QAǧam wa-l-Barbar, meaning: 'Book of Lessons, Record of Beginnings and Events in the History of the Arabs and Non-Arabs and Berbers'.

In India for example[START_REF] Fishman | Handbook of language and ethnic identity[END_REF].

Later, Ferguson (1996) himself will explain that by 'variety' he means varieties of the same language and not varieties in terms of 'socio-linguistic variables' such as variations which are diatopic, diastratic, diamesic, etc.

Tatsamas is a term, which is used by Ferguson, that means 'the same' and refers to the use of classicalisms appropriate to the phonological system of H in L varieties.

[START_REF] Durand | Dialettologia araba[END_REF] also adds that this model, which is basically valid for the Mashreq countries, is not perfectly applicable to the Maghreb, not least because it does not take into account the important role that the French language plays in cultural and intellectual spheres.

For studies on the Maghrebi ESA, see[START_REF] Benmayouf | L'arabe parlé par les cadres algériens (ou l'arabe algérien médian)[END_REF] regarding Algeria,[START_REF] Taine-Cheikh | L'arabe médian parlé par les Arabophones de Mauritanie: étude morpho-syntaxique[END_REF] regardingMauritania and Youssi (1986) for the Moroccan situation. For an analysis of the markedness of SVO-VSO orders in MSA, see[START_REF] Lancioni | Ordini lineari marcati in arabo un'analisi generativa[END_REF].

Here we report the terminology adopted by[START_REF] Daoud | The sociolinguistic situation in tunisia: language rivalry or accommodation[END_REF]. However, as noted by professor Francesco Grande, whom we thank for his observation, the taxonomy reported in this paragraph, presents an ambiguous reference to the Qur'anic Arabic and the later written literary usage, labelled Classical and Literary Arabic, respectively.

'Tunisia is a free, independent and sovereign state; its religion is Islam, its language is Arabic and its system of government is the Republic'. My own translation.

'Historically, such a register is due to disappear sooner or later, once education and administration have been arabised.' My own translation.

The scientific transcription given in[START_REF] Crystal | A Dictionary of Linguistics and Phonetics[END_REF] refers to the English realisation, but concerning the Greek, it is: [kOI"ne:].

See the Facebook page of Beit el Bennani: https://www.facebook.com/ Beit-el-Bennani-520104428117976/. Consulted on 4th April 2021.

In 1924, the orthographic code devised by the Gèaqda tal-Kittieba tal-Malti (Association of Maltese Writers) was adopted as the official alphabet of the Maltese language[START_REF] Cassola | A note on the dating of h, gh and x in maltese[END_REF] Vanhove, 2003).

This is my translation. I have tried to respect the structure of the original text.

The link to the Association Derja is as follows: http://www.bettounsi.com/. Consulted on 4th April 2021. All the contents are written in both encodings: the Latin script and the Arabic script.

For information about graffiti practices developed in Tunis during and after the Revolution, see[START_REF] Lacquaniti | I muri di Tunisi: segni di rivolta[END_REF].

For the definition of Arabizi, see section 1.3.3.

'Reflects the idea of resistance to the dominant adult or political order.' My own translation.[START_REF] Léglise | Parlers jeunes ici et là-bas[END_REF]

As in 1.1.3, Francophonie, in public debates since the revolution, is proudly flaunted by some and rejected by others, in a picture that is, as always, contradictory. Expressing oneself in French instead of Arabic, or vice versa, can be traced back to ideological choices, e.g. Islamists prefer Arabic, the Tunisian elite prefer French, etc.

See section 1.3.3 for a description of the Matrix Language Frame model developed by Myers-Scotton, who consider these forms as inter-sentential code-switching.

As we can see from this blog which sponsors the use of Yamli, one of the most popular of these input systems.https://alaashaker.wordpress.com/2009/03/14/ arabic-text-driving-you-crazy/. Consulted on 4th April 2021.

'a spontaneous and collective action [...] to acquire the reading and writing of a non-coded language'. My own translation.

Yamli's Smart Arabic Keyboard was launched in November 2007: https://www.yamli.com/fr/. Consulted on 4th April 2021.

Here is a link for Microsoft Maren's tutorial: https://www.youtube.com/watch?v= IWbYGTxy5x4&ab_channel=WaelKabli. Consulted on 4th April 2021.

[START_REF] Blau | The state of research in the field of the linguistic study of middle Arabic[END_REF] reports that, as in Judeo-Arabic script, [a]s far as possible, Arabic letters were marked by the phonetically corresponding Hebrew letters, including letters denoting allophones which phonetically resemble Arabic phonemes. When, however, no correspondence between Arabic and Hebrew existed, perforce the Arabic orthographic method is applied.

The use of the number six to encode the pharyngeal /t . / can be observed in the reference name /bat . t . āl/, i.e. 'unemployed', for the following Tunisian Facebook page: https://www.facebook.com/ ba66al/?ref=page_internal. Consulted on 4th April 2021.

In Tunisian Neo-Arabic, these are /t/, / T/, /d/, /D/, /r/, /z/, /s/, /š/, /s . /, /D ˙/, /t . /, /l/, /n/ and /ž/.

1.3. New Spaces for New Communication words made from the initial letters of other words, such as IRL, meaning In Real Life, or rotfl, meaning rolling on the floor laughing[START_REF] Crystal | Language and the Internet[END_REF] Androutsopoulos, 2011).[START_REF] Shortis | Gr8 txtpectations[END_REF] suggests that the non-standard orthography of texting almost certainly expresses the generally creative, playful and friendly tone intended by texters. This can also be detected in Tunsian DNW, where the playful tone is often realised by wordplay or the use of spelling and punctuation. Regarding the lack of abbreviations, these would probably hinder the comprehension of the written text, which, despite not being yet standardised, already presents a certain degree of difficulty on the level of comprehension. Indeed, it is also important to notice that CMC users have not only developed an individual writing style, but that different web-based channels also require different codes and metapragmatic awareness of the choice of written style.101 Arabizi is an interesting phenomenon in itself, but at the same time can also be a useful tool for the study of Tunisian in general, allowing for a study of a large amount of data. For this reason, it was chosen as the encoding for the Neo-Arabic Tunisian texts collected in the Tunisian Arabish Corpus, the building methodology of which will be described in chapter 2.101 In fact, one of the future studies we would like to carry out, on the advice of Professor Carmela Perta, whom we thank, could be a study of Arabizi encoding according to a functional pragmatic approach.

These French expressions respectively mean computer-mediated communication and mediated electronic discourse.

Setting and Scene, Participants, Ends, Act Sequence, Key (tone, manner, or spirit of the speech act), Instrumentalities (channels), Norms, and Genres.

For the role of corpus linguistics in gender in sociolinguistics studies, such as studies on gendered languages, see[START_REF] Mori | La sociolinguistica dei corpora per lo studio della lingua inclusiva di genere nelle varietà legislative dell'eurolect observatory multilingual corpus (francese, inglese, italiano, spagnolo, tedesco)[END_REF].

In this specific case, the different trends relate to the different amount of nouns, verbs, and adverbs employed.

Ariana, Béja, Sousse, Bizerte, Gabès, Nabeul, Jendouba, Kairouan, Zaghouan, Kebili, Le Kef, Mahdia, Manouba, Medenine, Monastir, Gafsa, Sfax, Sidi Bouzid, Siliana, Ben Arous, Tataouine, Tozeur, Tunis and Kasserine.

The following is the link to this linguists and lexicographers tool for language analysis: https: //www.sketchengine.eu/.Consulted on 7th April 2021.

Git is a distributed version control software usable from a command line interface, created by Linus Torvalds in 2005. TArC is available at the following link https://github.com/eligugliotta/ tarc.

The link to the licence is as follows: https://creativecommons.org/licenses/by-nc-sa/4. 0/legalcode. Consulted on 7th April 2021.

The algorithm which is used in supervised learning to implement error back-propagation consists of an application of the gradient descent method, where the contribution of each parameter to the model error is given by the partial derivative of the loss function with respect to that parameter.

This topic will be explored again in next section.

Reinforcement learning also exists, which aims at learning to select an action in order to maximise

The image is inspired by the unrolling explanation of[START_REF] Goodfellow | Deep learning[END_REF] and the Christopher Olah's blog: http://colah.github.io/posts/2015-08-Understanding-LSTMs/.

A hyperbolic tangent (Tanh) activation layer applies the Tanh function on the layer inputs, which is also a sigmoid function, that takes any real value as an input and outputs values in the range of -1 to 1.

In recent years, work with contextualised embedding trained on non-annotated data, such as BERT[START_REF] Devlin | Pre-training of deep bidirectional transformers for language understanding[END_REF], is increasing. Such research constitutes promising possibilities for improving many ANLP tasks. Some examples of this include Arabic BERT[START_REF] Safaya | Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech identification in social media[END_REF], AraBERT[START_REF] Antoun | Arabert: Transformer-based model for arabic language understanding[END_REF], GigaBert[START_REF] Lan | Gigabert: Zero-shot transfer learning from english to arabic[END_REF] Marbert (Abdul-Mageed et al., 2020).

CRFs are a class of statistical modelling method applied to the performance of conceptual tagging at word level. These models mainly exploit features based on n-grams[START_REF] Lafferty | Conditional random fields: Probabilistic models for segmenting and labeling sequence data[END_REF][START_REF] Dinarelli | Discriminative reranking for spoken language understanding[END_REF].

SVMs are supervised learning models with associated learning algorithms that analyse data for classification and regression analysis.

This problem has also been addressed for oral texts, i.e. in[START_REF] Ali | Automatic dialect detection in arabic broadcast speech[END_REF];Biadsy et al. (2009);Biadsy and Hirschberg (2009);[START_REF] Lei | Dialect classification via text-independent training and testing for Arabic, spanish, and chinese[END_REF], in that case where dialect identification is useful in developing robust speech systems, such as speech recognition and speaker identification.

Networks designed on the end-to-end principle collect application-specific features at the communicating end nodes of the network, rather than at the intermediate nodes, such as gates, that exist to build the network.

The assigned class labels are: MSA, Dialectal, Both, Foreign, Named Entity, Unknown and Typo.

http://www.ramitechs.com/. Consulted on 7th April 2021.

For further information about the translation process, see[START_REF] Bouamor | The madar arabic dialect corpus and lexicon[END_REF].

https://camel-guidelines.readthedocs.io/en/latest/orthography/. Consulted on 7th April 2021.

The bloggers' profiles can be visited at the following links: https://draft.blogger.com/ profile/08907253046329916251 and https://tounseyyadigordeya.wordpress.com/a-propos/.

The ParseHub website is at https://www.parsehub.com/.

The Tawèīd, reported in table 3.1, is the principle of the uniqueness of God, the first affirmation of the Muslim faith expressed in the šahāda, and a testimony of faith in Islām.

Among the two males, the youngest was from the Djerba area, and the other from Tunis; in terms of the female representation, the youngest woman was from the Zarzis area and the other was from Bizerte.

CODA* guidelines are available at https://camel-guidelines.readthedocs.io/en/latest/ orthography/.

As shown by preliminary experiments that yielded rather poor results, with below 50% accuracy on average.

The dictionary is available at the following link: https://github.com/hbenbel/ French-Dictionary.

The dataset is available at the following link: https://github.com/chaymafourati/ TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset.

Available at https://pypi.org/project/emoji/.

The syntheticity index of a language is a coefficient that describes, for the language in question, the degree of concentration of morphological functions within a word.

Version used: MADAMIRA 2.0. D3 BW* schemes[START_REF] Habash | Introduction to Arabic natural language processing[END_REF].

To be precise, we refer here to the notion of transfer as postulated by Tesnière (translation in French)(Tesnière, 2015, 46-58).

For more information about Universal Dependencies, see https://universaldependencies. org/introduction.html.

In particular, version 3.8 of 2009 was of great assistance.

The nisba adjective is the 'relative' adjective in Arabic, i.e. /tūnsiyy/ means '(smth. or sb.) of Tunis'.

On the other hand, for tokens currently classified as foreign, the same treatment is planned to be addressed in the future, i.e. adding a POS-tagging and lemmatisation layer for these as well.

The multi-task system is available at the following link: https://gricad-gitlab. univ-grenoble-alpes.fr/dinarelm/tarc-multi-task-system.

Experiments on the lemmatisation level are not yet complete, which is why we will not report percentages for this annotation level.

A CODA version of MADAR was recently published by[START_REF] Eryani | A spelling correction corpus for multiple arabic dialects[END_REF]. We are already planning to use this data for future projects.

The texts were first translated into Standard Arabic, which was the vehicular language for the retrieval of the Tunisian Arabic texts. This may generate some data bias in the collection phase.

The reference website is: https://github.com/eligugliotta.

The Tunisian governorates are considered to be the following: Ariana, Béja, Sousse, Bizerte, Gabès, Nabeul, Jendouba, Kairouan, Zaghouan, Kebili, El Kef, Mahdia, Manouba, Medenine, Monastir, Gafsa, Sfax, Sidi Bouzid, Siliana, Ben Arous, Tataouine, Tozeur, Tunis, Kasserine.

These analyses are always based on absolute frequencies, even if we only report percentages in the tables in the next chapter, for the sake of synthesis. Absolute frequencies can be observed using the scripts created for the analyses and made available on the github page (see footnote

26).26 The smaller the p-value, the smaller the margin of error of analysis, and the exact p-value is then reported in the analysis.27 The multi-task architecture is available at the following link: https://gricad-gitlab. univ-grenoble-alpes.fr/dinarelm/tarc-multi-task-system, while TArC and the analyses scripts are available at the following link: https://github.com/eligugliotta/tarc.

Reference website: https://github.com/eligugliotta.

'And here's my problem, when a new woman comes into the family, I start hating her, hating her strangely and for no reason!!'. My own translation.

[START_REF] Gibson | Dialect levelling in tunisian arabic: towards a new spoken standard[END_REF] states that /q/ and /g/ have phonemic status, considering the loan phoneme /g/; however, this is not taken into consideration in this analyses, which instead only focuses on the [g] as an allophone of /q/.

It would be interesting, for future studies, to compare the two different types of encoding in terms of this phenomenon. The rap lyric corpus in TArC has been collected with this comparative aim.

See, for example, the study of[START_REF] Metcalfe | Muslims and Christians in Norman Sicily: Arabic-Speakers and the End of Islam[END_REF] regarding Norman Sicilian Arabic and the practice of writing Arabic using another orthography, such as the Greek alphabet. See in particular the treatment of Arabic articles. Another example of this is Cypriot Arabic, and its definite article employments(Mion, 2017a,b).

Italian translation given by[START_REF] Caruana | Terminology of italian origin used in eu maltese[END_REF]: 'Il punto di maggior rilievo della politica regionale è quello di migliorare la coesione sociale e economica tra le regioni europee.'

These prepositions are mainly: .

The determinative morphemes are for Hebrew /ha-/, North-Arabic /h(n)-/ and Arabic /Pal-/(Garbini and Durand, 1994, 102).

The close relationship between these elements requires much more in-depth investigations that we have chosen to reserve for further dedicated study (seeGugliotta et al. (forthcoming)).

The query tools built for the spontaneous settling analyses are available from the same reference website: https://github.com/eligugliotta.

We would like to thank the reviewers of this work, Simone Ciccolone and Luca D'Anna, for their helpful comments and corrections regarding these linguistic elements.

Also called T-roles, which are mainly carriers of semantic cores.

The word Sahel (sāè@l, 'coast'), is used in Tunisia specifically to refer to the eastern region of the coast and its main cities.

Regarding table 4.17, percentages are given based on the total number of sentences selected by our query tool, which was 123. In this case, the script is available on the reference website: https: //github.com/eligugliotta, and the script output is also available in appendix A.2.1. For this specific data observation we considered only the sentences for which we had the gender information, namely 49 of the mentioned 123 sentences presenting code-switching.

[START_REF] Riegert | Activists, individualists, and comics: The counterpublicness of lebanese blogs[END_REF], regarding code-switching in Tunisian, showed that the mechanisms of language choice, code-switching, and linguistic variation that occur on digital platforms are the same as those that occur in offline interactions.

However, it must be kept in mind that the songs that are part of our corpus are almost all by rappers from Tunis.

Past participles were not excluded. In addition, we decided not to exclude words containing gemination of the semivocal, given a substantial number of occurrences of reduction and diphthong preservation in cases such as /sīd-i/ and /sayyid-i/ for 'my lord'.

An interesting phenomenon to investigate in terms of this data, which has a good amount of monophtongisation data (approximately 650 occurrences), would be to observe the occurrences of monophtongisation in /ī/ vs. /ē/ and of /ū/ vs. /ō/, and the treatment of /ī/ before the 3 rd s.f.p. pronoun suffix /-ha/. Regarding these phenomena, see[START_REF] Mion | Réflexions sur la catégorie des «parlers villageois» en arabe tunisien[END_REF]. However, because of time constraints, this analysis will be postponed to future studies.

in the same years, the distribution is: ⋆ 2: 0.84% ⋆ 3: 31.09% ⋆ 4: 0.28% ⋆ 5: 13.17% ⋆ 6: 0.0% ⋆ 7: 31.65% ⋆ 8: 0.56% ⋆ 9: 22.41% 158 is the arithmographs tot for years 2007-2008 in the same years, the distribution is: ⋆ 2: 3.16% ⋆ 3: 33.54% ⋆ 4: 0.0% ⋆ 5: 12.66% ⋆ 6: 0.0% ⋆ 7: 29.75%

⋆ 8: 0.0% ⋆ 9: 20.89% 307 is the arithmographs tot for years 2009-2010 in the same years, the distribution is: ⋆ 2: 0.0% ⋆ 3: 25.41% ⋆ 4: 0.0% ⋆ 5: 14.98% ⋆ 6: 0.0% ⋆ 7: 17.92% ⋆ 8: 6.51% ⋆ 9: 16.94% 754 is the arithmographs tot for years 2011-2012 in the same years, the distribution is: ⋆ 2: 1.33% ⋆ 3: 16.45% ⋆ 4: 0.0% ⋆ 5: 2.65% ⋆ 6: 0.0% ⋆ 7: 10.21% ⋆ 8: 0.0% ⋆ 9: 13.13%

in the same years, the distribution is: ⋆ 2: 0.84% ⋆ 3: 12.17% ⋆ 4: 0.1% ⋆ 5: 3.15% ⋆ 6: 0.1% ⋆ 7: 9.86% ⋆ 8: 0.21% ⋆ 9: 18.57% 871 is the arithmographs tot for years 2015-2016 in the same years, the distribution is: ⋆ 2: 1.38% ⋆ 3: 40.53% ⋆ 4: 0.46% ⋆ 5: 4.36% ⋆ 6: 0.0% ⋆ 7: 27.67% ⋆ 8: 3.67% ⋆ 9: 21.93% 651 is the arithmographs tot for years 2017-2018 in the same years, the distribution is:

⋆ 2: 1.38% ⋆ 3: 37.48% ⋆ 4: 0.31% ⋆ 5: 0.92% ⋆ 6: 0.0% ⋆ 7: 32.1% ⋆ 8: 0.15% ⋆ 9: 5.53% 1398 is the arithmographs tot for years 2019-2020 in the same years, the distribution is: ⋆ 2: 1.14% ⋆ 3: 41.56% ⋆ 4: 0.36% ⋆ 5: 13.88% ⋆ 6: 0.0% ⋆ 7: 24.03% ⋆ 8: 0.86% ⋆ 9: 18.17%

Acknowledgements

This work is the result of a path of growth, both professional and personal. Without the support and guidance I have received over the years, it certainly would not have been the same, both in terms of results and appreciation for the journey.

21773 > < NP : el # ns è > < POS : DET # NOUN > < idx : 21881 > < NP : bil # dlel > < POS : PREP + DET # NOUN > < idx : 22485 > < NP : fil # zitt > < POS : PREP + DET # NOUN > < idx : 26171 > < NP : fil # jografia > < POS : PREP + DET # NOUN -NSUFF_FEM_SG > < idx : 26187 > < NP : el # sa7el > < POS : DET # NOUN > < idx : 26219 > < NP : mel # jenoub > < POS : PREP + DET # NOUN >

Rap metadata can be registered in two ways. In fact, rap lyrics are collected and written down by fans, but the text of the lyrics is a product of the singer. In this case, the decision was made to register the information of the user, where it was made available, considering it to be more informative in comparison with information about the singer. This is because we collected rap lyrics taking into account the possibility to study their orthography in comparison with the same song transcribed in Arabic As shown in these tables, despite our efforts to construct a corpus which is as representative as possible of the language in multiple of its aspects, it is not possible to obtain balanced percentages, except at the cost of forcing the data collection in a specific direction at the cost of the representativeness of the other language dimensions. Therefore, as stated in the previous chapters, we have opted for the least invasive choice, namely, a snapshot of reality, which is that of DNW in Tunisian Arabizi through different textual genres.

As far as the analyses are concerned, the solution imagined here in order to ensure the validity of the results found was to take into account this information regarding the percentages, and at the same time to use appropriate statistical tools. For example,

Pearson's chi-squared test [START_REF] Pearson | on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling[END_REF][START_REF] Fisher | On the interpretation of χ 2 from contingency tables, and the calculation of p[END_REF] is a statistic test that measures the statistical relationship between two categorical variables. (14) entouma yelzemkom derss fil jografia, /ntūma y@lz@mkum d@rs fi-ž-Prep.det žuGr . āfya/, N 'You (pl.) need a geography lesson'.

Regarding example (11), the Arabizi prep-det compound bil is separated from the N dlel by a white space, while in the Arabic script, both prep and det should be written attached to the noun, i.e. , /b-@d-dlāl/. The situation is similar to that in example ( 14), where the prep-det compound fil is separated from the N jografia, while in the Arabic script, the PP should correspond to , (prep det+N), or at least to , (prep+det+N), which are both pronounced /fi-ž-žuGr ˙āfya/. The first Arabic-script realisation (prep det+N) reflects the Arabic morpho-syntactic system, while the second (prep+det+N) can mirror the pronunciation. The reason for prep+det N realisation, instead, could be the result of language contact, facilitated through the use of the Latin alphabet (section 4.3.2).

The same situation is represented by Maltese PP, in which we can find the same compounds (Prep+det) as in Tunisian Arabizi, but joined to the noun through a hyphen (-), resulting in Prep+det-N as in the following example ( 26

Patterns

Occ. %

1. [prep+det n] 320 58.3%

2.

[prep+det+n] 128 23.3% By observing table 4.1, it is possible to notice an orthographic structure of the PP that seems to be placed in an intermediate position with respect to the extremes of the continuum; this is the structure in which all the elements are attached into a single orthographic word ([prep+det+n]). It is our belief that this solution can be considered as being representative of Arabic orthography (in the case of proclitic prepositions of the first type), as well as representative of the oral realisation in Tunisian. On the other hand, as far as the fourth structure is concerned ([prep det n]), we do not believe this to be representative of either of the two realities, and given the scarcity of representation in percentage terms (8.2%), we consider same to be not very indicative for the purposes of our analysis.

In order to verify whether the nature of the Tunisian preposition, matching Arabic proclitic ones, plays a role in the orthographic realisation of the syntagma in Arabizi, which is considered useful for observing the internal composition of these prepositional syntagmas. Taking into account what has been said about prepositions in MSA, two prepositions in particular, i.e. a proclitic one ( ), and an independent one ( ), which are among the most frequently used, were chosen as the focus for this analysis. In table 4.2, the frequency of the two chosen prepositions and the percentage of their occurrences in the different types of orthographic realisations of prepositional phrases shared model, which seems to coincide with the Tunisian model (table 4.30).

Finally, looking at the Tunis data in table 4.31, it can be seen that if diastratic variation still exists, i.e., if the realisation of diphthongs is something typical for old women in Tunis, as noted by [START_REF] Singer | Grammatik der arabischen Mundart der Medina von Tunis[END_REF] and reiterated by [START_REF] Gibson | Dialect levelling in tunisian arabic: towards a new spoken standard[END_REF], this was not captured in our corpus, where the results indicate a rather clear male prevalence.

Gov. of Tunis

Appendix A Output Data Appendix

A.1 Quasi-orality Analyses

The query tool used to perform the analyses outlined in this section is available at the mentioned website (https://github.com/eligugliotta) and it is named 'QuasiO-ralityAnalyses.py'. The following is the output of the script built in order to obtain the result presented in section 4.2.

According to the pattern selected, writing its code, such as 'pp6', as shown in the example, it is possible to observe the complete internal composition of the pattern. We report the composition of the defined PPs.

Pattern 'pp3' ([PREP+DET+N]):

Arithmographemes Percentages

The following is the output of the script built in order to obtain the arithmographemes percentage presented in section 4.

A.2.1 Code-Switching Analyses

Regarding the analyses presented in section 4.3.2, the query file is named 'trends_CodeSwitching.py'.

The script produces a txt file with all sentences containing a Tunisian article followed by a name classified as foreign. The generated file can also be downloaded from the relevant website. The file structure corresponds to the file reported below, which shows the first two sentences of the txt file. The script also produces the percentages shown in the table 4.17 and Pearson's chi-square test between code-switched NPs and user gender, and between code-switched NPs and textual genres.

A.3 Text formality degree Analyses

Regarding the text formality degree, the query script is named 'formality_degree.py', for the general analyses performed in order to verify the existence of the formality degree. The same script it was also used to observe the Nominal Phrase distribution and the percentages of table 4.37. We report below some data regarding the NP distribution, that is also possible to observe using the script.