Paweł Stacewicz

André Włodarczyk
email: wlodarczyk.andre@gmail.com

To Know we Need to Share -Information in the Context of Interactive Acquisition of Knowledge

Keywords: information, knowledge, ortho-information, meta-information, para-information, semantics, pragmatics, ontology

Natural language interface requires much more complex processing than is currently assumed. We present a general review of foundational notions of data, information and knowledge, aiming at tentatively sketching out a set of subcomponents of an integrative linguistic theory of man machine interaction, bearing in mind that our model will favor further research on simulation of man man interaction. Our main concern is to show that in order to acquire knowledge humans need to extract it from multiple kinds of information as distributed in the content of natural language utterances.

Introduction

Contemporary Natural Language Processing (NLP) techniques are mostly based on syntactic parsing which relies on the highly simplified view of interpretation as the interface between morphological form and semantic content. We propose to pay more attention to the fact that, though quite insufficient for representing meaning of language messages, linguistic knowledge is much more autonomous than is usually admitted in language sciences, psychology, logics and computer science (in both 1st and 2nd generation AI). Indeed, the semantic content of utterances is, in most approaches, directly engulfed in cognitive knowledge. We are going to show that producing and understanding information as conveyed by natural language messages requires more complex processing. Assuming that natural language utterances are always partial, we therefore reconsider radically the access to the semantic content of utterances by the distinction sometimes made between the overt (or explicit) content, being only a small part of integral information on the basis of which the covert (or implicit) content is supposed to be reconstructed. In the Distributed Grammar (DG) program, it is assumed that the interpretation of linguistic utterances requires taking into account at least three tiers of information (para-, ortho-and meta-information) and that these informational spaces are multiplied in such a way as to provide insight into cognitive semiotics as separate (though not independent) from multi-modal cognitive semantics.

In section 2, we discuss the most ambiguous today concept of information and describe the processes which lead from data expressed in some language to knowledge through the interpretation of data which transforms them into information that has to be verified in order to become a part of knowledge 1 . In section 3, we present the complexity of information expressed in natural languages. Natural language information consists of three tiers.

Between information and knowledge

Variety of meanings of the term "information"

At the current stage of civilizational development -more and more often referred to as the age of information and/or information society [START_REF] Ito | Birth of joho shakai and johoka concepts and their diffusion outside Japan[END_REF] -the term "information" is very common and also ambiguous. It is used primarily in communication contexts (when it comes to transmitting information by means of various types of media) and technical contexts (when it comes to processing information by means of computers and devices linked to them).

Despite the strong dominance of these two contexts, the attempt to define a maximally general concept of information 2 requires the consideration of at least three of its dimensions (in other words: semantic areas), which refer in turn to: (a) the world, (b) cognitive systems (including the human mind), (c) language. Let us explain this in more detail.

Ia. In the physical dimension, information is understood as something in the world, that is, as a certain way of ordering or organizing, the material elements that form specific structures [START_REF] Stonier | Information and the Internal Structure of the Universe[END_REF]; it is therefore a certain component of being, related to Aristotle's form. Ib. In the cognitive dimension, information is understood as something within the cognitive system [START_REF] Casagrande | Information as Verb: re conceptualizing Information for Cognitive and Ecological Models[END_REF], i.e., as cognitive content that can become identical to knowledge if the additional condition of a sufficiently good justification is met (given that the content is a statement of something). Ic. In the communication dimension, information is meant as something within a language [START_REF] Cherry | On Human Communication[END_REF], i.e. the content conveyed by means of a sequence of characters that are understandable to the recipient, such as gestures, verbal constructions, mathematical formulas, computer data, etc.

It is worth noting that today there is another way of understanding the term "information" as a specific type of code that can be stored in the memory of a computer, processed according to specific rules, transmitted, used to control external devices etc. [START_REF] Primiero | Information in the Philosophy of Computer Science[END_REF]. This meaning can be found in phrases such as 'information processing systems' or simply information systems.

In this work, we do not treat the above-mentioned semantic area as an alternative to the ones listed above (Ia-Ic), because we believe that today computing systems already perform certain cognitive functions, such as object recognition, categorization, reasoning or learning [START_REF] Russel | Artificial Intelligence: A Modern Approach[END_REF], and in addition, they do so in a way that is increasingly close to cognitive autonomy (e.g. when they are based on self-organized artificial neural networks). Moreover, the content which is computationally coded, e.g. by means of artificial programming and communication languages, fits well within the dimension Ic; and as far as computational virtual objects are concerned, intertwining more and more closely with the physical world, they should be included within the dimension Ia [START_REF] Chalmers | The Virtual and the Real[END_REF].

Information as cognitive content

Among the three aforementioned meanings of the term "information" we will focus here on Ib, i.e. information as cognitive content, which we will abbreviate to cc-information. By referring the above meaning to the cognitivecommunicative activity of the human being -which for the time being is a blueprint and a desirable "point of destination" for artificial cognitive systems -we can see that the notion of cc-information combines all the above-mentioned meanings. This statement is justified by the following reconstruction of a characteristic human cognitive process that leads from structures and regularities observed in the world (see Ia) to information communicated, conveyed and shared by a certain community (see Ic). Here are the successive stages of that process [START_REF] Stacewicz | O redukcji informacji do danych (On reducing information to data)[END_REF]:

The human mind: i) finds in the world (i.e: in the signals/stimuli that reach it) some regularities that appeal to it and which most probably are natural, ii) reflects these regularities in a certain internal code (in the brain appearing as a neuronal record), combining with this code a certain meaning, iii) can make of this code, once interpreted, a meaningful element and/or base of its own knowledge, iv) can transcribe the interpreted and meaningful code (sometimes having the status of knowledge) into a certain intersubjectively accessible language and communicate it in this form to other cognitive subjects in order to interact with them effectively.

If we focus on the above-mentioned processes -especially points (ii) and (iii), which combine the concepts of cc-information with knowledge, we can refer to a scheme known from the theory of knowledge as the DIK3 Pyramid.

To begin with, we will briefly discuss a certain typical interpretation of it [START_REF] Burgin | Theory of Information: Fundamentality, Diversity and Unification[END_REF]).

The pyramid consists of three levels, the layout of which corresponds to the following observation: the information processed by the cognitive system is, on the one hand, encoded in a specific way and, on the other hand, can become the basis or element of knowledge of the system.

According to this observation, the lowest level of the pyramid is filled with data, i.e. certain uninterpreted chains of symbols of a specific code (e.g. language); above them is information, i.e. interpreted (meaningful) data, inter alia, in terms of the goals defined or achieved by the system; the level of knowledge, in turn, is built up on top of information -i.e. all the information verified by the system, and thus sufficiently well justified for it. The notion of justification is naturally associated with the activity of the human mind, which consciously refers to facts and/or inferences "embedding" new information in the system of accumulated knowledge. Nothing however stands in the way of using this concept -just like the concept of c-information -in isolation from the conscious activity of the subject, referring to purely formal relationships between elements of knowledge (and information aimed at being integrated into knowledge). With this approach, the automaton can also generate and communicate justifications. In addition, if the automaton is equipped with receptors and effectors that give it contact with physical reality, it can generate justifications based on observation of that reality.

Therefore, the pyramid comprises three levels -I. data, II. information and III. knowledge -which are grounded in hard facts, i.e. what is happening in the world around the pyramid4 . To enter level I. the ability to recognize purely physical regularities in the world (which are reflected in data) is needed; to move from level I. to level II. the interpreting activity (of data) is necessary; and from level II. to level III. -verification or justification of the veracity of the information obtained.

In the sequel, we will sketch out the main points of the new model of the structure of linguistic utterances as elaborated within the framework of the Distributed Grammar program following which the explanation of utterances shows how the uninterpreted sign systems (standing for something else) differ, in the context of the communicating agents cognitive activity, from the meaningful and verified components of knowledge.

Information in natural language

The view of information in natural language that we present hereafter is based on our research within the Distributed Grammar program (DG) 5 which led us to conceive a meta-grammar of natural languages seen as interfaces rather than as operational codes, the productions of which are arbitrary ad hoc projections into (possibly multi-modal) representations of knowledge. Distributed Grammar [START_REF] Włodarczyk | Meta-Informative Centering in Utterances -Between Semantics and Pragmatics[END_REF][START_REF] Włodarczyk | Qu'est-ce au juste que la prédication ? (What actually is predication)[END_REF] is an integrative theoretical framework aiming at accounting for the relatively complex structure of simple utterances which is usually explained using terms such as topic, focus, subject, argument, predicate in separate theoretical proposals.

Information is often seen as a part of knowledge. As such it is always presented with respect to language: "analyzing the concept of information, it is natural to come to the conclusion that information has three major aspects or issues: Syntax, Semantics and Pragmatics" [START_REF] Burgin | Theory of Information: Fundamentality, Diversity and Unification[END_REF]. However, we point out that the above triad combines syntax with semantics and pragmatics in spite of the fact that semantics and pragmatics are information, but syntax is not. In our view, ontology is closer to semantics and pragmatics than syntax which, in fact, applies to information belonging to all of these domains: ontology, semantics and pragmatics. We consider that information selected from each of the above 3 cognitive domains characterises respectively the three tiers of the conceptual structure of utterance content we present here: para-information, ortho-information and meta-information (POM).

In our linguistic theory, the material form (sounds) of even very simple natural language utterances (in addition to their referral to prosody, phonology, morphology and syntax) expresses content consisting of at least three kinds of information: (1) the ortho-informative ("properly" semantic) signification simultaneously with (2) the para-informative (ontological) identification and (3) the meta-informative (pragmatic) process of predication (in a strictly linguistic sense) 6 . Moreover, research on several natural languages, showed that what is immediately conceived and perceived during the synthesis and analysis of the most elementary language messages (utterances), is their meta-informative structure even before one gets some access to all other kinds of information. As a matter of fact, ortho-and parainformation are less salient and need more processing power for producing and understanding linguistic utterances.

Hereafter is a schematic representation (fig. 1) of the three kinds of information and the relations between them as expressed in a simple utterance (1):

(1) Brutus killed Caesar with a poniard.

Information components in the figure above are not all overtly expressed by a segment of the utterance, some of them remain covert. The morpho-syntactic form of the utterance gives the most direct access to the meta-informative content. On this tier, we find the nouns Brutus and Caesar which, in English, can be recognised -on the basis of their position before or after the verb -respectively as the subject and the (direct) object. The subject is that attention-driven phrase (ADP) which refers to the speakers global centre of attention (CA). The verb and its complements are what is being said about the subject, they are the constituent parts of what we call predicate. As a matter of fact, in the DG framework, the predicate reflects the meta-informative clustering of representation [START_REF] Włodarczyk | Qu'est-ce au juste que la prédication ? (What actually is predication)[END_REF]. Consequently, predication is not a semantic notion but a pragmatic one, since meta-information depends on the speakers point of view, and more precisely, his own centres of attention presumably shared with the hearer.

In utterance (1), Brutus is the subject noun phrase (reduced to only one noun (proper name) without any determiner such as article or adjective), and it occupies the position of the global attention-driven phrase of this utterance pointing at the main centre of attention (CA). In other words, this utterance is first of all about Brutus. The predicate contains three constituents: a verb (whose content, in fact, as a rule, belongs primarily to ortho-information) and two noun phrases. The first one is the direct object Caesar which -from the point of view of the meta-informative structure of utterances -is the local attention-driven phrase pointing at the secondary CA; this means that in this utterance the main concern is Brutus and the secondary one is Caesar. Within the predicate, the second noun phrase with a poniard, is the so called "indirect object". However, these kinds of phrase can hardly be considered as the "third" attention-driven phrase (ADP) for the following reasons: firstly, the English preposition with -as used in this contextexpresses the means of an action (in our terminology the "median role"). Hence, it conveys ortho-informative content and, secondly, according to [START_REF] Oberauer | Selective attention to elements in working memory[END_REF], a neurologist of attention, no more than two centres of attention (one global and one local) can be active at once, the third one seems to be impossible.

As concerns the two attention-driven phrases, i.e. the subject and direct object, the semantic role each of them plays in the situation expressed by the verb is not directly marked by its syntactic position. As a matter of fact, Caesar could be as well expressed not as the direct object of the verb in active voice, but as the subject of the verb in passive voice. The passive voice would make it possible on the level of meta-information to treat Caesar as the main CA and Brutus as the secondary one:

(2) Caesar was killed by Brutus.

In utterance (2), the semantic roles of Caesar and Brutus are the same as in utterance [START_REF] Burgin | Theory of Information: Fundamentality, Diversity and Unification[END_REF]. But how do we know what their roles are? Knowledge about these roles is included in the verb valence 7 , not in the morphological form of noun phrases nor in the syntactic structure of the utterance. The valence scheme of the verb kill contains three slots and English speakers know from the lexical meaning of this particular verb, that when it is in the active voice, its subject refers to the active role (the killer) and its object to the passive role (the killed), whereas the median role is partly expressed overtly by preposition with in the indirect object.

As shown on fig. 1, roles belong to utterance semantics, i.e. to its ortho-informative tier 8 . Native speakers learn the meaning of verbs together with the roles that participants play when taking part in the situation expressed by the verb. The semantic roles of the subject and object of a verb may however be different than that of action verbs like kill, depending on the semantic content of the verb itself and depending on its voice form, e.g. ; the subject of a verb with the meaning of feeling something refers to a passive participant, e.g. (3). (3) Old people suffer from the heat.

The interpretation of the utterance content must also include the identification of entities spoken about, which takes place in the para-informative tier. On this level, we find individual concepts, for instance concerning entity classes entailing animate, human and non-human beings or inanimate things, and conceptual schemes expressing relations between entities, i.e. situations and the roles they contain. In utterance (1) Brutus and Caesar both belong to entities characterised as male human and adult agents, and the individual features of each is rooted in the readers knowledge about antique Rome where Caesar was an army general, political leader and writer; Brutus is known as Caesars adoptive son. The poniard belongs to inanimate things (figures) made of metal and used as weapons. Thus, in the para-informative tier each of the three noun phrases refers to a well identified entity.

In the para-informative tier, a selection procedure leads to the extraction of individual concepts and relational schemes out of the speakers knowledge. Then, they are embedded into the ortho-informative tier as participants playing roles in a chosen relational scheme, a frame which makes it possible to configure the situation spoken about. In each language, the situation frame is expressed by the valence schema of a verb. However, elements which have been identified in the para-informative tier and configurated in the ortho-informative one belong to a different sort than the components of the meta-informative structure which is closely related to the speech act and particularly to the speakers and hearers knowledge. On the meta-informative level, elements of para-and ortho-information are in turn selected and ordered depending (a) on the speakers centres of attention and also, as far as shared attention is concerned, on the hearers centres of attention, as well as (b) on the specific means the speakers have at their disposal in the language they speak. As will be shown further, these linguistic means consist both of valence schemas and grammatical recombination rules (verb voices, major constituents order etc.). Integration of information belonging to the three different tiers is made possible by the fact that participants and spatio-temporals anchors of the situation are identified as referring to the same entities. Table 1 summarizes the operations characteristic of each information tier.

In order to end successfully with comprehension, the interpretation of an utterance must access the orthoinformative tier of semantic representations. Selecting participants and/or spatio-temporal locations together with their respective views produces para-information. Ortho-information is generated when speakers make connections between entities in their mental representation space (configuration of situations). Meta-information (predication) takes place when speakers express their mental representations in linguistic utterances within which some fragments of ortho-information are distinguished thanks to attention centering. Recall however that information is only partially expressed as a result of application of the base schemes (valences) whose instantiations undergo various transformations which are subsequently mixed up and multiplexed in order to be exteriorised in the form of utterances.

Meta-information

The meta-information tier is the one of attentional partitioning of ortho-information into 1 st -degree metainformation and 2 nd -degree meta-information for the purpose of discriminating central and peripheral parts of it. The first level of meta-information concerns that of base utterances whose centres of attention are expressed by subject (global) and object (local). The second one concerns extended utterances in which the global ADP is the topic and the local one the focus. The most important property of topic and focus is their respectively old and new status of communicated information which is in contrast with the rest of the utterance. As a matter of fact, although this might seem at first glance contradictory with the definition of "information", communication in natural languages is based on the alternation of old and new9 . Moreover, the old or new status is not an inherent property of ortho-information but is based on the way in which it is communicated. The speaker is free to introduce some chunk of information either with a new or old meta-informative status: this is a major argumentative device in the strategy chosen to enrich or manipulate the hearers knowledge.

In the DG framework, base and extended utterances are defined as pragmatic units of discourse in contrast to simple and complex sentences understood as syntactic units [START_REF] Włodarczyk | Subjecthood and Topicality are both Pragmatic Issues[END_REF]. As a pragmatic unit, each utterance contains at least one attention-driven phrase (ADP) referring to a centre of attention (CA). The ADP may have either the same or a different meta-informative status (old or new) than the rest of the utterance (the statement). In a base utterance there is no contrast between the status of the global ADP and that of the statement: it is either "all new" or "all old". On the other hand, the ADPs of extended utterances contrast with the statement. The topic bearing an old metainformative status is in contrast with the new comment, the focus of new meta-informative status is in contrast with the old background. When a constituent of a base utterance (be it an ADP or another NP) undergoes topicalization or focalisation it is treated as an extension of the base utterance which, in many languages, is moved to the left or right periphery of the base utterance 10 . In so-called free word order languages (e.g. Polish, Russian), topic and focus are superimposed on other NPs mostly by means of prosody and word order.

Concepts coined for the description of meta-information have been elaborated using algorithms from Formal Concept Analysis -FCA [START_REF] Wille | Restructuring Lattice Theory: an Approach based on Hierarchies of Concepts[END_REF] and Rough Set Theory -RST [START_REF] Pawlak | Information Systems -Theoretical foundations[END_REF][START_REF] Pawlak | Rough Sets. Theoretical Aspects of Reasoning about Data[END_REF]) as implemented in the Semana platform at CELTA 11 . It is worth noting that only two features (the old or new statuses, and the global or local property of attention-driven phrases) suffice to show the variety of attention-driven phrases in the form of a lattice (fig. 2). Base utterances are generated using valence schemes. In order to change the meta-informative structure of an utterance, languages make use of recombination rules some operating within base utterances and the others making it possible to build extended utterances. Recombination rules may be either lexical or grammatical. Lexical means of recombination are pairs of related verbal schemes, such as buy/sell, give/receive, thanks to which the same participants in a situation are introduced as global or local centres of attention. Changing the verb makes it possible to change the subject within a base utterance. Verb voice (active/passive) belongs to the meta-informative grammatical means used in recombination, cf. utterances (1) and [START_REF] Casagrande | Information as Verb: re conceptualizing Information for Cognitive and Ecological Models[END_REF]. With the passive voice, the distinction of salience (global/local) is changed without changing the ortho-informative content of the utterance. The recombination of ADP by topicalization or focalisation makes it possible to build extended utterances: the most universal means are prosody and word order; particles; syntactic constructions (such as cleft sentences for focalisation) are also commonly used [START_REF] Włodarczyk | Meta-Informative Centering in Utterances -Between Semantics and Pragmatics[END_REF]. Hereafter are two examples of the possible ways of topicalizing or focalizing one of the constituents of utterances (1) and ( 2).

(4) It is Brutus who killed Caesar with a poniard. (Brutus is focalized by the cleft sentence form: it is who) (5) As concerns Caesar, he was killed by Brutus with a poniard.

(Caesar is topicalized by the expression as concerns)

Moreover, in the DG framework, both the so called rigid word order (like in English) and free word order languages (like in Polish) can be dealt with in a consistent way: in both types of languages, the typical word order or, more precisely, major phrase order is a meta-informative marker used to introduce ADP and to distinguish between old and new information. In the first type of languages, word-order is concerned mainly with the first meta-informative level (subject and object of base utterances) and in the second type with the second meta-informative level (topic and focus of extended utterances). Yet, in no language does word-order serve only in base or extended utterances exclusively. Moreover, it must be emphasized that besides word-order, intonation is the most universal marker of meta-information.

Ortho-information

The ortho-information tier is determined by an encapsulation/activation function within a certain information frame (a configured network of information) concerning basically the roles of participants of the spatio-temporally anchored situation in question.

In the ortho-informative tier, information about roles played by participants and spatio-temporal anchors is configurated into situation frames. Conceptual schemes are mental representation frames through which it is possible to pick up and identify situations of the outer world. In the process of linguistic communication, conceptual schemes are matched with valence schemes, sort of ready-made linguistic frames making it possible to express oneself very briefly. In human languages, there is no direct access to ortho-information in an utterance: only literal meaning is explicit whereas cognitive content is barely alluded to and has to be reconstructed in the process of semantic interpretation. The semantic content of linguistic categories is distinct from their cognitive content; for instance, gender is not sex, nor tense in grammar is time in human experience, nor grammatical number matches directly the cognitive notion of quantity. Within situation frames, participants (expressed by noun phrases in the syntactic positions of subject, direct object and indirect object) are cast in active, passive and median actancy. But it has to be emphasized that in DG, the term actancy refers to a linguistic category which is kept apart from the non-semiotic cognitive notion of agentivity. Linguistic actants are characteristic of the language semantics but do not match directly cognitive roles of agents (animate entities) and figures (inanimate) in the conceptual representation of agentivity. Moreover, in DG, agentivity is not restricted to agents playing active roles as it is the case in contemporary linguistic theories 12 , it concerns all sorts of roles played by participants in a situation, i.e. not only active but also passive and median roles. Actancy roles are explicit while agency roles have to be interpreted using world knowledge shared by the speaker and the hearer as shown in example ( 6).

(6) The door creaked.

In utterance [START_REF] Halliday | Intonation in the Grammar of English[END_REF], the subject door is an active pseudo-role; as a matter of fact, when we think it over, it is clear that the inanimate figure door is not an active participant of the situation ; we know from our experience that doors have to be moved by an active participant (be it an agent, e.g. a person, or a figure, e.g. the wind) in order to creak.

Actancy is a very abstract concept. For this reason, it generates a certain degree of ambiguity. On the contrary, agentivity is more concrete and precise. In human languages, actant roles are attributed to participants according to valence schemes. The core of syntax is made of valence schemes which speakers use "en bloc" and learn as fixed 13syntactic structures. As a matter of fact, the knowledge of a language provides speakers with valence schemes (actancy) that make it possible to bring together information from the three layers (albeit partially) in a single expression. In 20th century research on natural language syntax, it has been shown that only a small part of linguistic utterances can be fully generated by inference rules while the biggest part are fixed non-compositional constructions. Linguistic utterances are built both with syntactic rules and valence schemes.

The main components of the ortho-informative tier are situation frames, actant roles and spatio-temporal anchors referring to the time and place in which a situation takes place (more on ortho-information in [START_REF] Włodarczyk | Les Cadres des situations sémantiques[END_REF][START_REF] Włodarczyk | Roles and Anchors of Semantic Situations[END_REF][START_REF] Włodarczyk | Roles, Anchors and Other Things we Talk About: Associative Semantics and Meta-Informative Centering Theory[END_REF][START_REF] Włodarczyk | Meta-Informative Centering in Utterances -Between Semantics and Pragmatics[END_REF]).

Para-information

The para-information tier is the space of overtly expressed concepts which are established with regard to other concepts such as alike (similar or opposite), but not expressed in the utterance.

Although identification is always present in the para-informative layer, it is rarely expressed otherwise than by naming an individual or a relationship. Morphemes such as "just", "also", "even" and "only, solely, merely" are used as relative identifiers which may be explicitly expressed in natural language utterances (Figure 3). Para-information concerns therefore the identification of concepts (among them those of entities and situations) when they emerge in the speakers mind not only in reflexive terms (in relation to themselves) but also in a relative way as a kind of 'alikeness' (as compared to other members of the same group of beings, class of figures, location in space and time etc.). Thus, at this level, in order to communicate information, speakers transmit what they know about both individual as well as relational cognitive and ontological concepts. Here again, the selection of these concepts is guided by the verb valence patterns, but it is carried out first in mental representations before it is expressed in an utterance. It should be noted that adverbs, particles and deictic pronouns constitute the language expression of this identifying selection in the broad sense in which equivalence and complementarity also participate (as sufficient criteria of the distinctiveness of the semantic features). It seems interesting to note that logicians who choose to work on logical reasoning with identity give the identity relationship a separate status among all other relationships.

Conclusion

Modelling information as transmitted by natural languages, one needs to consider that: (a) knowledge components and communicated information are distributed in multi-stratified modules of the human mind, (b) information build up out of formal concepts (defined as duality between sets of objects and sets of features) represents the content of language utterances, (c) information contained in language utterances is stratified and (d) natural languages are interfaces but, due to the numerous changes during the incredibly long evolution, they have their "purely" linguistic semantics which is partial, and therefore needs developments in the "core" multi-modal knowledge.

For building artificial systems which would be supposed not only to cooperate efficiently in a certain environment but also to communicate with humans, it is necessary to take into account how humans actually obtain and convey information (cognitive content) using linguistic expressions. In this paper, we have presented a semiotic conceptual grid covering three types of information as encoded in natural language utterances. Being developed in depth in more advanced studies, the proposed solutions may reveal to be an important contribution of non-IT researchers to purely technical investigations on information systems with natural language proficiency.

Taking into account any cognitive system, including artificial ones, various types of interactions can be considered, not limited to the exchange of information between human minds. Equally important is human interaction with the computing systems they, in fact, actually create. Without it, for example, it is difficult to imagine practicing modern science (we have in mind here improving and automating calculations, using digital models and simulations, andat least partly -making and verifying hypotheses. It is also worth considering computing systems, e.g. multi-agent systems, which either operate on the basis of internal interaction between their modules or in interaction with other similar systems [START_REF] Maudet | Commitment-based and dialogue-game-based protocols: new trends in agent communication languages[END_REF].

Fig. 1 :

 1 Fig. 1: Three information tiers and their mutual dependencies as expressed by utterance (1).

Fig. 2 :

 2 Fig. 2: Lattice revealing the interdependencies of attention-driven phrases as used in language utterances.

Fig. 3 :

 3 Fig. 3: Para-information concerns identity and alikeness

Table 1 :

 1 Intra-tier operations on information within utterance content.

	Sort of information	Intra-tier operation	Result
	meta-information	Centering	Distinguished part of information driven by the centres of attention
	ortho-information	Configuration	Proper information content: situations & participants
	para-information	Identification	Components of semantic situations: entities & relations

Some fragments of this article are based on two works published in Polish:[START_REF] Stacewicz | O zwiazkach informacji ze sfera wartosci ze szczegolnym uwglednieniem idei racjonalnosci (On the relationship between information and the sphere of values with a particular regard to the idea of rationality)[END_REF] and[START_REF] Stacewicz | O redukcji informacji do danych (On reducing information to data)[END_REF].

In this work we will not deal with the general concept of information, which can (perhaps) be abstracted from the above-mentioned semantic areas but will focus on information understood as cognitive content (Ib). It is worth noting that among the types of meanings which we pointed to, the most controversial is to consider information as something physical that exists in the physical world (Ia). As regards the ambiguity of the term "information" see also[START_REF] Floridi | Information. A Very Short Introduction[END_REF].

Data-Information-Knowledge.

Interestingly, all the three key concepts of the DIK pyramid are called, in certain contexts, information. Data is often viewed as basic information, being the basis for further analyses and conclusions (e.g. data as a mathematical problem); interpreted data, called here simply information -is information in a semantic and pragmatic sense (having a certain meaning and somehow used by a processing system, including the human mind); and knowledge is understood as information in a strong sense (e.g.: scientific publications are both a record and a source of knowledge).

This research was developed (2000-2014) at Centre de Linguistique Théorique et Appliquée (CELTA), Sorbonne University, Paris.

N.B. The term predicate is used differently by logicians[START_REF] Włodarczyk | Qu'est-ce au juste que la prédication ? (What actually is predication)[END_REF].

Valence refers to the capacity of a verb to take a specific number and type of arguments (noun phrase positions). SIL Glossary of linguistic terms.

Research on ortho-information was first introduced under the name of associative semantics in[START_REF] Włodarczyk | Les Cadres des situations sémantiques[END_REF][START_REF] Włodarczyk | Roles, Anchors and Other Things we Talk About: Associative Semantics and Meta-Informative Centering Theory[END_REF].

"Connected speech unfolds as an unbroken sequence of "messages", in which the speaker is alternating between elements of given and elements of new; these map into the structures of the other grammatical units, most powerfully into those of the clause." ([START_REF] Halliday | Intonation in the Grammar of English[END_REF], p. 42).

In German, the topic and focus are moved outside the limits of the syntactic structure of base utterances, to the first and to the last position in the utterance. In English, most often, topic is moved to the left periphery of the utterance whereas focus is mostly marked by intonation.

See Interactive Linguistics at Centre for Theoretical and Applied Linguistics (Centre de Linguistique Théorique et Appliquée -CELTA), http://celta.paris-sorbonne.fr/anasem/indexIL.html

Agent is the semantic role of a person or thing who is the doer of an event. [...] A prototypical agent is conscious, acts with volition (on purpose), and performs an action that has a physical, visible effect. SIL Glossary of linguistic terms, https://glossary.sil.org/term/ agent-semantic-role(19.03.2020)

In most cases, valence schemes have been generated by ancient grammar rules which are no longer in use.


Mario Laurent
email: mariolaurent.univbp@yahoo.com

Project Hatemeter: helping NGOs and Social Science researchers to analyze and prevent anti-Muslim hate speech on social media

Keywords: Online hate Speech, Anti-Muslims hatred, Twitter monitoring, NLP applied on Social Sciences. 1. Hatemeter

Hatemeter' is a European project funded under the Rights, Equality and Citizenship Programme. The project aims to document, analyse and prevent hate speech on social media. It brings together researchers from several disciplines as well as NGOs in each of the three partner countries: Italy, France and Great Britain. Initially the researchers' efforts focused specifically on anti-Muslim hate speech on Twitter. Following the recommendations of NGOs active on the field, the Hatemeter Platform was developed. It allows the automatic collection of large amounts of data on social media. Thanks to it, a user can: identify potential hate messages in real time, visualize the data collected through different types of graphs, and receive suggestions for counternarratives writing. Used by Social Science researchers, it has proven to be an effective tool for exploring large amounts of data, identifying trends and then supporting more qualitative analytical approaches. For NGOs, it facilitates social media monitoring, stimulates and accelerates the training of operators who produce counter-narratives to fight hate speech.

(REC Action Grant, project reference: 764583). The present article is part of the dissemination phase of the project and aims to share the approach we followed and some of the results of the project. This article is an extended and improved version of the one presented at the TWSDetection-2020 workshop [START_REF] Laurent | Hatemeter Project: Analysis of hate speech on twitter at the crossroads of computer science, humanities and social sciences[END_REF]. The project ran for two years, from March 2018 to February 2020, with the main objective of creating an ICT tool (the Hatemeter Platform) to automatically collect data from social media, produce graphs to analyze this data and help users write counternarratives to the hate speech they encounter. To this end, the University of Trento, Italy, coordinating the project, the Capitole University of Toulouse, France, and the Teeside University, UK, brought together researchers in criminology, social sciences, law and linguistics. The Bruno Kessler Foundation (FBK), a research institute in Trento, was responsible for developing the Platform thanks to a team of NLP (Natural Language Processing) researchers.

The team's efforts have focused on the specific case of anti-Muslim hatred and exchanges on the social media Twitter. However, the project defined itself from the outset as a proposal for a methodology to analyze Internet discourse and is intended to evolve extending its activities to other hate speech (homophobia, anti-Semitism, etc.), in other EU countries and other social media (a YouTube video monitoring module was already added). This work is therefore either to be prolonged, within a research group called 'Hatemeter Lab', or to be replicated/improved by other research teams.

Steps of the project

The project can be divided into three main phases, over the first 6 months, each team studied the specific context of its country. This study included a state of the art of scientific literature, identification of the laws related to the subject and qualitative interviews with on the ground actors, engaged in the fight against discrimination and hate speech against Muslim. This step ended by writing advices and suggestions on the features of the Hatemeter Platform, in addition to its initial specifications.

Then, the development phase of the Platform was initiated and lasted about a year, including testing sessions with the NGOs partners in the three countries involved in the project. The main test phases took place at the end of January and in mid-June 2019, in each NGO's facilities, with operators and volunteers. After testing the tool, this operators and volunteers filled in reflexive evaluation survey to highlight the platform's strengths and weaknesses in terms of design, usability and efficiency, in order to guide developers to improve it. Criminologists and sociologists also provided feedback to the development team as new functionalities were added.

Finally, as a last step, each partner was able to use the platform to carry out its own analyses, with the main objective of producing counter-narratives, for NGOs, and understanding the construction and dynamics of hate speech for researchers. All results, the final report [START_REF]Hatemeter Project Homepage[END_REF], as well as intermediate reports and details on the Platform development can be found on the project website [START_REF] Di Nicola | HATEMETER: Hate speech tool for monitoring, analysing and tackling Anti-Muslim hatred online[END_REF].

Context of study

Specific History

We will briefly present the differences in the context and results of the project in the three countries involved and we will pay particular attention to the case of France.

Anti-Muslim discourses in Italy are very strongly associated with anti-migrant discourses as the country is experiencing the biggest migratory crisis in its recent history. Indeed, Italy occupies a crucial place on the road to the EU for populations coming from both Africa and the Middle East. Moreover, unlike other European countries that have had a Muslim population for several decades, the issues raised by the cohabitation between religions are relatively new in Italy [START_REF] Sciortino | Islamofobia all'italiana[END_REF] [START_REF] Serdar | Islamophobia in Western Europe: A Comparative, Multilevel Study[END_REF]. The concerns raised by both the humanitarian crisis and recent terrorist attacks in Europe are omnipresent in political discourse and the social media. This context facilitates the development of hate speech (xenophobia, anti-Muslim hatred, anti-Semitism, racism, etc.) [START_REF] Bortone | L'hate speech al tempo di Internet[END_REF].

The UK and France, for their part, share a colonial history which has led to greater ethnic and religious mixing, for decades, but which has sometimes given rise to contentious opposing these different populations. Both countries have also suffered directly and repeatedly from terrorist attacks perpetrated by extremists in recent years. This can also fuel discourse of mistrust towards Muslim citizens in general. However, differences in societal models (multiculturalism in the UK and secularism in France), policies, laws and history make the two grounds quite different, as we will see in the discourse analyses conducted with the Hatemeter Platform.

Previous research has shown that UK far right-wing groups (such as the English Defence League and Britain First) have quickly appropriated social media to develop systematic discourse to incite hatred against the Muslim population [7] [START_REF] Awan | The affinity between online and offline anti-Muslim hate crime: Dynamics and impacts[END_REF]. In addition, online spaces are often used to extend speech out of the realm of what is acceptable in the mass media, as well as to mix private and public affairs, gradually blurring traditional boundaries [START_REF] Allen | Findings from a Pilot Study on Opposing Dudley Mosque Using Facebook Groups as Both Site and Method for Research[END_REF]. The field of acceptable discourse within the spectrum of freedom of expression, limited both sides by the discourse of extremist groups, is sometimes referred as 'Overton window' [START_REF] Szalek | Some praxiological reflections on the so-called Overton window of political possibilities, framing and related problems[END_REF]. In another research, Dematteo illustrate how some far-right French and Italian groups play a role in shifting this so-called Overton window on the social media, following the idea mentioned previously of re-formatting public opinion by blurring or moving the traditional boundaries [START_REF] Dematteo | Marine + Matteo: L'étreinte populiste[END_REF].

French law context

In France, several laws play a role in how we can deal with online hate speech. First, hate speech directed against any person based on their origin or religion is strictly prohibited, both in the case of direct physical or verbal attack and in the case of incitement to hatred, including using electronic devices. This is stated by the Law of 1881 on media freedom [START_REF]Law on 'liberté de la presse[END_REF] and reinforce by a decree of the Penal Code from 2005 [START_REF][END_REF] that specify that these discourses are also forbidden in a private space.

Then, the Law on the Separation of Church and State is often used as a justification for excluding or demanding the exclusion from the public space of any person wearing visible religious symbols. In the case of French Muslim citizens, it is veiled women who are regularly targeted, although this law does indeed concern institutions and not individual citizens, whose freedom of worship is affirmed by the same law [START_REF]Law on ' la séparation des Eglises et de l'Etat[END_REF]. The debates around the interpretation of this law create perpetual controversy between different political groups and sometimes feed the hate speech on social media.

Finally, the Law on ethnical statistics can be both a protection and an additional difficulty to study and understand hate speech. As it prevents to gather any statistics on religion or ethnicity, it ensures a blind and ethical treatment between citizens when processing computerized data on a large scale [START_REF]Ordonnance on 'statistiques éthniques[END_REF]. However, the lack of official government statistics is often used by groups stigmatizing Muslims to exaggerate their numbers in France and give credence to the thesis of a massive cultural replacement that would threaten traditional French values. Despite this law, UK polling institutes can still carry out surveys on the Muslim population in France and on the estimation of this population by French. According to their study, the estimated rate of Muslims in France in 2016 was 31% while the actual rate was only 7.5% [START_REF] Duffy | Perceptions are not reality: what the world gets wrong[END_REF]. In addition, while the Muslim population in France at the end of 2017 was estimated at 5.7 million individuals, some far right groups predicted a number of 20 million [START_REF] Banet | 20 millions de musulmans en France ? Ils sont environ 4 fois moins, selon les estimations les plus sérieuses[END_REF]. This illustrates the use of the lack of data to play on the fear of a clash of civilizations, theorized by some far right-wing activists.

In July 2019, a new law was debated by the National Assembly, which should make hosts responsible for the hate content published on their platform, including social networks and media (Twitter, YouTube, Facebook) [START_REF]Law on 'Lutte contre la haine sur internet[END_REF]. The legislative field is therefore currently undergoing a transformation around these issues of hate speech, particularly online. It should be noted that the term 'Islamophobia' originally used in the draft law has been replaced by 'anti-Muslim hatred', for fear that there might be confusion between a ban on incitement to hatred against individuals and a ban on criticism of religion itself, which would not be compatible with freedom of opinion [19]. This illustrates the terminological disagreement surrounding incitement to hatred, which is difficult to resolve: much discrimination is indeed on a blurred line between hatred of a person because of their religion, racism, and hatred of religion that extends to the people practicing it. On this point, recent research analyzing the evolution of political discourse in the assembly [START_REF] Tahata | Définir « les Français », une question de race ? Analyse des formes de racisation dans les débats parlementaires sur la nationalité et l'immigration (1981-2012)[END_REF], as well as the discourse of extremist groups online [START_REF] Froio | Race, Religion, or Culture? Framing Islam between Racism and Neo-Racism in the Online Network of the French Far Right[END_REF], shows a tendency in hate speech directed against "the other", designated as a common enemy of the nation, to abandon biological racial criteria and rely increasingly on cultural and religious criteria.

The implications of Twitter analysis

As any other communication channel, Twitter is a tool that has its limits and rules. A message conveyed on Twitter is therefore, at least, affected by the media and in some cases quite far from the author's thinking, in order to serve their objectives. If it is presented as a tool for sharing information and opinions, Twitter is above all based on a system where access to information depends on the profiles followed by its users. This results in a race for audience and provokes the implementation of communication strategies.

One important characteristic is the maximum size of a Tweet, limited to 280 characters. If many users use the "thread" technique (responding to themselves to increase the possible length of their speech), the mechanics of the media still encourages to write in a concise and punchy way in order to be relayed, and therefore read. Longhi has studied the relationship between the form and content of a message and its effectiveness (number of responses and retweets) [START_REF] Longhi | Tweets politiques: corrélation entre forme linguistique et information véhiculée[END_REF]. He concluded that this effectiveness seems to be directly related to the controversial nature of its content. Other researchers have studied the propagation in social media of verified news and fake news and have shown that fake news will both reach more people and spread faster [START_REF] Vosoughi | The spread of true and false news online[END_REF].

According to our observations, other strategies, such as systematically responding to much more followed profiles by expressing an opposite opinion or exaggerating one's opinion, are widely used to increase one's visibility/number of followers. Finally, the use of subtle and understated discourse, or irony, knowing that the audience will understand the true intentions of the message, is used recurrently in the networks we observed and is a challenge both for automatic analysis and for human analysis, if they are unfamiliar with the community and the subject in question.

Hatemeter Platform

Interviews and preliminary observation

Before developing the platform, each research team conducted in-depth interviews with on the field actors in the three countries involved. These interviews allowed us to identify groups that produce anti-Muslim hate speech as well as the hashtags and keywords they use. In France, interviews and observations allowed us to identify two main forms of anti-Muslim hatred: first, impersonal discourse based on negative stereotypes associated mainly with North Africans descent and culture; secondly, cyber-harassment campaigns that target specific individuals, having appeared in the media as Muslim personalities. The main objective of such campaigns is to silence Muslim citizen and force them out of the public/media sphere. Generally, harassment follows a recurring pattern: a visible religious sign or reference appeared in a media. This sign is associated with a willingness of its bearer to proselytize and immediately seen as an indication of belonging to extremist movements. Finally, the person is quickly seen as an accomplice or sympathizer of terrorist groups. After being identified as a threat by a group of harassers, the victims can be pursued relentlessly, receiving private messages or numerous negatives comments under each of their Tweets. The private messages can go from insults to death threat. Some of the interviewees were exposed to such threat or/and have had their addresses or private photos revealed on harassers group pages. Figure 1 is an example of a message published on Twitter that illustrates the first type of process mentioned above. The text says that the strikes in the Paris metro in December 2019 are due to the fact that the employees are Muslims, that they despise Christmas and consider people as farm animals. Figure 2 is an example of the second type of process, in this case a cyber-harassment campaign conducted against employees of the Decathlon sports store, after it was announced that it would be selling a "hijab running", a veil for Muslim women suitable for running. In this private message, the employees are accused of complicity in a Muslim invasion, for the purpose of getting rich, and are threatened to be exterminated in crematoriums.

Interviews conducted in the three countries with associations working in the field reveal the absence of an automatic collection tool that would make it easier to identify both viral content and day-by-day posts inciting hatred against Muslims. In every country, interviewees note an increase in hate speech on social media and a difficulty in reporting it either to hosting platforms or to the justice system, as it is already the case for offline hate crimes. The NGOs interviewed being involved both online and offline in the field underline a continuity of hate speech between the two spaces, one of which can be reinforced by the other, with online events continuing with aggression and vice versa. The challenge for these associations is to respond to negative stereotypes and hatred rather than ignore them, to prevent these ideas from continuing to spread easily.

Platform Features

The Hatemeter Platform is a tool that serves three main purposes: to collect data on Twitter, stored in a database, to provide tools to its users in order to visualize this data, and to provide counter narratives to online hate speech. The platform has been developed by the FBK team, including Sara Tonelli and Marco Guerini (see [START_REF]Hatemeter Project Homepage[END_REF]).

The Platform collects data on Twitter and YouTube. For Twitter a list of hashtags/ keywords often used by hate groups is used, but also some more "neutral" hashtags that simply talk about anti-Muslim hatred (such as "Islamophobia"). In each language, all Tweets displaying the targeted hashtags and keywords are collected daily, along with metadata such as user, date, number of retweetings, number of responses, etc. All this information is then reused by the platform to create graphs allowing the user to analyze the discourse and the networks behind this discourse. These graphs also allow diachronic or synchronic comparisons. All features have been programmed to be as much as possible language-independent, available in Italian, French and English, they could easily be reused for additional languages.

The following Table 1 was elaborated by the FBK team, it summarizes the number of each kind of elements extracted from social media to build the Hatemeter database. The Platform includes five features applied on Twitter. Recent trends gives access to a list of all Tweets of the last 24 hours for a selected hashtag or keyword. This feature does not use the database but retrieves live messages from Twitter. The platform displays all tweets in a tab on the right side of the screen and also calculates a hashtags co-occurrence graph from the one chosen as the central node.

The Hashtags trends feature shows similar results, but based on the database. Messages that are no longer viewable on Twitter are therefore used to construct the co-occurrence graph for a hashtag/keyword and a period of interest. It is completed by day by day statistic and display of the most retweeted messages displaying this same hashtag (see Figure 3). The co-occurrence graph can be moved or zoomed and the nodes display the names of the hashtags they represent.

The Hate speakers feature looks exactly the same, choosing a hashtag/keyword for the desired 10-day period. The only difference is that the statistics and co-occurrence graph are for specific users rather than hashtags/keywords. This feature allows, on the one hand, to identify networks of people interacting and possibly specific groups holding a discourse on recurring themes. On the other hand, it allows to have statistics on the number of retweets around an individual and thus to discern the real influencers of those who follow them. This is also useful for estimating the audience of certain speeches, by coupling this data with a diachronic analysis, which can be carried out by keeping the same hashtags/keywords and changing the period of time observed. The Counter-narratives function allows the user to enter a hate message, either by typing it on the keyboard or by selecting it directly from one of the other features of the platform. The user is then presented with a list of four possible responses, from which they can choose, or decide to edit one. In this case, it can be saved in the database. It is this function of the platform that provides concrete assistance to operators in the prevention of hate speech. By providing standard counter-narratives messages the platform can guide users who want to intervene, when they discover incitement to hatred posts on social media, but do not know how to respond.

The database contains counter-narratives initially written by the operators of the three NGO partners in the project, based on stereotypical samples of hate speech. The platform associates each new hate message with one of its initial stereotypical messages, thanks to a similarity calculation based on the tf-idf formula, and can thus suggest the most relevant answers possible among those available. When the user is satisfied with their counter-narrative, they can copy/paste it to post it with their Twitter account (the Platform never publish something by itself).

Finally, the Alerts feature allows the user to view the number of Tweets with a selected hashtag/keyword, day by day, over the entire period of time it was present in the list monitored by the platform. Figure 4 is a view of this feature.

First results

Italy and the UK

Thanks to the Platform, Italian researchers identified three main themes generating anti-Muslim hate speech on Twitter. Firstly, tweets following terrorist attacks, secondly exchanges around the fear of foreigners and finally, concerns about the migration crisis, the latter two themes can be mixed together to give rise to the fear of being invaded. The recurring vocabulary of their corpus of analysis was classified into three categories: 'Us vs them', 'Dehumanising adjectives' ('parasitic', 'shit'...) and 'Social Threat' ('terrorist', 'invasion'...). According to Amnesty International Italy, which was our NGO partner in Italy, the analysis ran with the platform confirmed that politicians, even if they use a moderate tone in their Tweets contribute to a large part of the production of discriminatory content (one in five tweets).

For the English team, the main themes identified through the tool are: Muslims seen as invaders, Muslims seen as pedophiles or sexual exploiters, Islam seen as evil or as an ideological cult rather than a religion. They also find many uses of degrading adjectives or dehumanizing nouns to refer to Muslims. It should be noted that they also found traces of numerous counter-narratives, including ironic counter-narratives.

Finally, with regard to the English NGO involved in the project, Stop Hate UK, they reported many positive points about the use of the platform. According to them, using it well requires training, but it makes it easy to spot illegal content and hate groups on Twitter. Some operators have used the platform to report content or enter into discussions with users by offering counter narratives. The counter narratives tool makes it easier and faster to write counter speech, and it has been proven an excellent tool for training new operators.

France

The main themes in anti-Muslim hate speech, identified through the platform in France, are: Muslims seen as a threat associated with terrorist attacks, Muslims seen as a cultural invader who wants to impose their norms, and Muslims as delinquents. Regarding the second theme, it often takes two forms: the customs of Muslims, especially the wearing of the veil by women, are perceived as an attack on secularism, and Muslim organizations are perceived as politicized and extremist.

Seeing the potential of the platform as a tool to support qualitative analysis, we have determined a step-by-step methodology to make the best use of the platform's functionalities. Starting with a large-scale analysis of a Hashtag, with the 'Alert' feature and then determine how it is used, how often it is used, what audience it reaches, the user networks and finally, find examples of use for each Hashtag. It is even possible to understand better the group of interest by tracking back actual Twitter accounts.

On the figure 4, the alert function has been used on the hashtag 'Islamophobia'. It has made it possible to identify a number of events that are either the source of anti-Muslim discourse or, on the contrary, denounce anti-Muslim acts. Using then the Hashtags trends function, we were able to identify that the most important peak corresponds to the Christchurch attacks in New Zealand. The co-occurrence graph then allowed us to identify a hashtag often used in reaction to these attacks: '#grandremplacement'. Indeed, the terrorist who carried out these attacks had referred to the theory of the great replacement to justify them. As this theory was first formulated by a French extreme rightwing writer, this information provoked a huge reaction from both Muslim defenders and hate propagators in France.

Figure 5 shows the user co-occurrence network for #grandremplacement for a time-span showing a lot of exchanges, which we had identified earlier through the 'Alert' function. This network is very dense and shows communities organized around influencers, influencers relayed by scattered individuals and other isolated individuals. We can see two main influencers for the communities in brown (top right) and white (bottom right) and four influencers for the community in green. Going into detail, these communities represent different far right-wing groups, with slightly different ideology and themes. Interestingly, the user identified with the most connection (information we can have thanks to the table on the right) is none of the nodes salient on the graph, since he has been relayed by people from various political and ideological backgrounds, not linked to each other (we have blurred user names).

A qualitative analysis, done by looking at user profiles and their use of the hashtag #grandremplacement shows that it is used on a daily basis, relayed by a few dozen to a few hundred users per day. In addition, it is applied to various subjects that are not necessarily events on a national scale, always with the aim of denouncing an invasion, often by Muslims, which would be organized and harmful for 'native' European populations. Figure 6 gives two concrete examples of this hashtag use to manipulate information, following a near-conspiracy logic, to prove the harms of foreign cultures, while pretending to present only 'facts'. The Tweet on the left relays statistics concerning violence against women in France, and comments by saying that the 'Islamization' of society causes France to lose its women's right and other historical values. The Tweet on the right mentions the destruction of Middle Ages' bridge, in Belgium, in order to get bigger boats across the river. The user comments that it happens because the 'new' Belgians, from the 'great replacement', have no respect for historical heritage.

Pros and cons of the Platform

It is difficult to capture real hate speech through the search for keywords or hashtags because Twitter users do not want to be legally concerned and they are trying to avoid any obvious hate words. They will often resort to a subtle and sophisticated speech. Their tweets can hide behind the right to free speech to express some hateful views. They also can claim that they state a 'fact' to better give their opinion.

However, there is still a phenomenon well captured with our methodology: we can identify all the tweets that looks like an innocent content but are using a hashtag that specify their membership to a certain ideological group. Usually, this type of tweets makes not much sense unless we can understand what the hashtag referred to. Our methodology also allows us to regularly discover new hashtags thanks to our co-occurrence hashtag view. Then, we can expand our keywords/ hashtags list of collection. In general, the tone of a tweet is also difficult to determine, sometimes a Muslim defender will use a hashtag usually reserved for hate propagators in an ironic way and sometimes it will be the opposite. It is therefore interesting to collect a wide range of tweets in an automated manner, but never to abandon qualitative analysis to assess the real nature of the speeches.

In addition, in order to measure NGO operators' satisfaction with the platform, we asked them to rate 26 items using a satisfaction scale ranging from -3 to +3. These questions were grouped into categories: Attractiveness (is the design good?) Perspicuity (is it easy to use?), Efficiency (does it help to monitor hate speech?), Dependability (does the user feel in control of the tool?), Stimulation and Novelty. Statistical analysis of the results showed that one aspect was rated below average (Dependability), while the others were rated good to excellent (Stimulation and Novelty). This shows that a fairly simple tool focused on social media monitoring, visual representations and suggested responses, can be seen as new and stimulating for NGO operators who without a tool can be overwhelmed by the amount of hateful content and discouraged by the feeling of not having the means to respond. However, the poor assessment of dependability also shows that it is necessary both to train operators in the use of the tools provided to them, but also that the platform is not yet flexible enough in terms of user control over the content analyzed.

Discussion and perspectives

Although the project does not introduce major technological or theoretical innovations, its originality and interest lies in the multidisciplinary nature of the research teams, the cooperation with on the field NGOs to develop credible and applicable tools, and the international collaboration that makes it possible to highlight common needs as well as differences between European countries. The project also uncovers many questions inviting further research. It should be note that certain very recurrent themes are specific to a country, such as the theme of women veiled for France and that of Muslims portrayed as pedophiles in the UK. One hypothesis is that the stereotypes associated with the Muslim in each country are linked to historical events, origins of these populations and intra-national events. In our example, the prototypical Muslim populations in France are of North African origin, while the usual English stereotypes refer to Pakistani populations. Finally, the reference to pedophilia is due to cases of pedophile networks involving Pakistanis in England, while these cases had little echo in France. The methodology proposed by the Hatemeter project could be applied to other analytical work on other types of online hate speech. With the Platform system, interactions between qualitative researchers and developers must be frequent to keep the hashtags/ keywords in the database up to date. We could imagine automatic proposals made by the platform to update these lists, based on new hashtags appearing in the co-occurrence graphs of hashtags/keywords already monitored. The greatest research effort still needs to be made on the counter narratives. The idea of having them written by NGOs operators was good but limited: they must be trained, they must be able to step back on the subject to adopt the best reactions and they must have a minimal understanding of the haters and their background in order to communicate with their Twitter followers. However, some of the participants in the creation of the counter-narratives database were emotionally very involved in hate speech, and responses were produced that could reinforce the polarizing effect of online discussions, while not hindering hate. We have therefore filtered out the proposed counter-narratives by tagging messages (too emotional, too religious, etc) to limit this problem. Indeed, we assume that counternarratives are more directed at third parties than at influencers themselves, as they will be difficult to shake up in their ideology.

One of the interesting possibilities of this platform feature is the supervised machine learning that can take place when counter narratives are edited and saved in the database by a user. The platform makes it possible to retain both this modification and the hate message to which it had to respond. Then, thus improve the accuracy of the counter narratives suggestion tool as it goes along. However, an evaluation should be carried out on the counter narratives in order to take advantage of this functionality. The following ideas could be the subject of an effectiveness measure:

• The provision of graphics, or links to articles, that can be used to dismantle fake news or prove false stereotypes • The use of humor to demonstrate absurd reasoning • The use of positive narratives about success stories or the normal daily lives of those targeted by hate speech • Make reference to the similarity between the family histories of those targeted by hate and those of haters Recent work on countering fake news about science on social media suggests that two simple strategies for getting individuals to reconsider their point of view have a positive effect: reestablishing facts in the face of preconceived ideas and dismantling the rhetorical arguments of influencers [START_REF] Schmid | Effective strategies for rebutting science denialism in public discussions[END_REF]. It remains to be seen whether these methods may extend to stereotypes about populations rather than scientific findings.

In addition to these points, the platform is already, as it is currently presented, an interesting tool for the training of new NGOs operators and, although it has not been created for this purpose, a very interesting tool for Social Science researchers without computer science knowledge, to support and facilitate qualitative analyses of social media discourses.

Figure 1 A

 1 Figure 1 A tweet, before being deleted, that illustrates stereotypical-based hate speech.

Figure 2 :

 2 Figure 2 : Screenshot of a private message received by Decathlon employees, which have been shared on Twitter, that illustrates death threat associated to a so-called complicity with an Islamist invasion.

Figure 3 :

 3 Figure 3: Screenshot of Hatemeter Platform, Hashtag trends for #remigration on the 07 September of 2019.

Figure 1 :

 1 Figure 1 : Screenshot of Hatemeter Platform, 'Alerts' function applied on #Islamophobie.

Figure 2 :

 2 Figure 2 : Screenshot of Hatemeter Platform, 'Hate speakers' function applied on #Grandremplacement.

Figure 3 :

 3 Figure 3 : Screenshot of Twitter, two tweets that illustrate how the #grandremplacement hashtag is used to convince other users that Muslims are a threat to European civilization.

Table 1 .

 1 Number of social media posts in the database by type and language(11/2019)

		Tweets	Replies	Retweets	N. of unique	YouTube	YouTube
					users	videos	comments
	French	159,822	272,847	1,222,710	261,039	782	101,982
	English	132,402	111,477	1,023,743	520,507	497	37,180
	Italian	33,736	28,362	179,834	47,816	611	32,655

The Hatemeter Project, subtitled 'Hate speech tool for monitoring, analysing and tackling Anti-Muslim hatred online' is a project with a The project was funded thank to the European Commission within the framework of the Rights, Equality and Citizenship Programme