Annie Gentes
email: annie.gentes@telecom-paristech.fr

Ted Selker
email: ted.selker@sv.cmu.edu

Beyond Rhetoric to Poetics in IT Invention

Keywords: Rhetoric, Poetics, Project Definition, Design, Sociology of Science, Sociology of Technology, Invention

Two kinds of discourse typically define scientific productions: logical (epistemology of science) and rhetorical (sociology of science). We suggest that research projects can also be analyzed as poetical productions. While rhetorical strategies anticipate controversies and deploy techniques to defend projects and findings, poetical practices deepen the cultural and symbolic dimensions of technologies. Based on use cases that show different ways the poetics come to bear on research and development projects in information technology (IT), we discuss the play on words and images and how they contribute to the definition and creation of a new technology within research projects. Three cases of poetical practices are presented: naming technologies, christening projects, and designing logos. We give examples of naming and project identity formation to underscore how such a poetic stance impacts projects. Images and words help people imagine what the technology is about by giving imaginary traits and cultural substance. This paper's analysis is a call for further work exposing the value of conscious use of poetical approaches to deepen the framing of IT projects.

Introduction: From the Rhetoric to the Poetics of Science

Science endeavors to create and test hypotheses [START_REF] Chalmers | What Is This Thing Called Science? Third Edition[END_REF]. Scientific contribution is then based on logical demonstrations that distinguish it from social and other common knowledge discourses. The epistemologies of sciences detail the methods of validation of what they build as facts, either true or false. Inductive and deductive methods are discussed. But science is also about strategies to defend a territory, to plead for a cause, to affront enemies, to gather followers. This strategic part of science has been extensively analyzed by the sociology of science after Latour [START_REF] Latour | Science in Action: How to Follow Scientists and Engineers Through Society[END_REF]. Actors build a fullfledged rhetorical strategy to support their project. The sociology of sciences and technologies [START_REF]The Handbook of Science and Technology Studies[END_REF] has shown that making science implies defending it and therefore elaborating a rhetoric that is going to convince the stakeholders. The father of rhetoric, the Greek philosopher Aristotle, describes rhetoric as an art of persuasion that every citizen should learn so as to debate about things in the city. In society, things are being discussed and judged. Winning the audience is the focus point of rhetoric.

Latour's contribution has been to analyze the form and role of scientific rhetoric. In particular, he shows that scientific papers are built around a defensive strategy that finds allies as a way to prevent dismissal. He describes how researchers use rhetorical arguments from theoretical background, "enrolling" other papers that have been written so as to prevent attacks. Latour speaks of "stratification" [START_REF] Latour | Science in Action: How to Follow Scientists and Engineers Through Society[END_REF] that is to say the building of a system of authority within the paper. This system also excludes those researches that do not follow the same direction. Research and scientific activities are therefore described like a court of law, where participants build up the defense of their case. For Latour, this rhetorical strategy makes it difficult for scientists to produce certain types of discourses. In particular, he points out that popularization is difficult because scientific texts are written to exclude people not to include them. What is more, scientific controversies need strong positions that do not leave space for dreams or imagination. However, certain texts (including images like drawings, photographs and videos) that are produced in the course of a research project cannot be totally described by either their deductive or inductive building of facts, nor their rhetorical function. Why call a technology Bluetooth after a king of Denmark? Why choose a cartoon character -Popeye -to represent an adhoc mobile communication research project? And why tell all kinds of stories when one might expect to communicate technical information by concentrating on algorithms and material implementations? We suggest the following answer: research done to study an area and engineering done to create technologies are not only based on logical demonstrations, or by negotiation between actors, they benefit by being subjective and cultural associations, poetic activities related to humanistic and literary practices too. They are using and working on symbolic representations that expand the observations and definitions that researchers obtain within their methodological framework and constitute an alternative to the rhetorical inscription in society by a poetical invention of concepts and narratives. Though we describe practices that have been experienced by most researchers in HCI and engineering, these practices can be analyzed as an integral part of the research process rather than discarded as communication, or valorization practices. Our standpoint is that these practices that focus on the subjective and cultural associations need to be considered as an essential part of the technical research as an invention. We consider invention along Simondon's definition. The philosopher of science and technology defines invention by articulating imagination to cultural and social context through a tangible artifact [START_REF] Simondon | L'invention dans les techniques[END_REF].

This article contrasts the poetical and the rhetorical, and their role in the emergence of new technologies. We contend that engineers, designers and scientists expand their work with their literary and humanistic skills that we propose to call their poetic stance. Ignoring the poetic aspects of their work is detrimental to a clear understanding of how it comes about. It could also be detrimental to strategies of innovation that have to take into consideration the whole gamut of activities around invention and creation to make sure that the potential of technologies are explored.

Our goal here is to show the value of poetics in IT projects. We first start from the poetic stance from Aristotle's founding text and a recent body of literature on poetics. We then present several use cases that are samples of this poetic stance: naming of technology, christening of a project, and design of logo. The analysis of these use cases shows interactions between the poetic naming and narrative process (with words or images) and the projects themselves.

A Poetic Stance

Before analyzing some of these poetic productions of science, we propose to start on the basis of Aristotle's definition of "poetics" and how it has been developed in literary theory in order to understand what is at stake in these images and stories that use more fictional metaphors and concept visualizations than either fact based demonstrations or rhetorical arguments [START_REF]Aristotle: Poetics[END_REF]. Then we proceed to support our argument for poetic practices with examples of these poetic productions. Like rhetoric, poetics is an art of production. While over the centuries, poetics has been reduced to a collection of stylistic traits and can still be considered as such (the poetics of literary genres, like science fiction for instance) more recently, poetic studies have focused on the creative process itself and its specificity [START_REF]The New Princeton Encyclopedia of Poetry and Poetics[END_REF]. In particular, the linguist Jakobson proposes a model of language that takes into consideration its different functions. Among them, the poetic function of language refers to itself, in other words is its own material. It is opposed to the referential function of language that points to things [START_REF] Jakobson | Linguistics and Poetics[END_REF]. Referential and poetic functions of language are further developed by semioticians, who distinguish denotation and connotation. Denotation is about the literal meaning and essentially descriptive while connotation is the subjective and cultural associations [START_REF] Barthes | Elements of Semiology[END_REF]. The poet and literary analyst Paul Valéry described this poetic stance happening when the purpose is not to use language as a persuasive means to create a social consensus but to transcend the here and then, because the writer or artist unfolds either latent or possible aspects of the world [START_REF][END_REF]. For Meschonnic too, specialist of poetry and translation, the creator questions what could be [START_REF] Meschonnic | Pour la poétique[END_REF]. This production can be called prospective, not because it foretells the future but because it produces new meaning. Aristotle uses the word Dunamis or dynamis that means possibility or capability. It is not an abstract vision. The design work consists of processing, working on, and with a variety of materials: either language (styles, genres [START_REF] Genette | Introduction à l'architexte[END_REF], or pigment or sound [START_REF] Gentès | The Design of Emotions: How the Digital is Making us More Emotional[END_REF]. Playing with words, inventing stories, making images are not only there to teach, or to defend, or to demonstrate. They expand the way we think about the world. The artist taps into and reveals the potential of a medium to transform and interpret the world.

Such inventive practices within research projects have recently been the subject of social studies of scientific texts [START_REF] Olohan | Scientific and Technical Translation[END_REF], in ethnography for instance [START_REF]Writing Culture: The Poetics and Politics of Ethnography[END_REF], and of imaging and visualization. Scientific images, for instance, have been noted for their aesthetic ambition [START_REF] Sicard | Chercheurs ou Artistes ? Entre Art et Science[END_REF]. Sociologists of science, Burri and Dumit point out the "engagement" that images produce and in particular how they are made instrumental in the production of scientific knowledge [START_REF] Burri | Social Studies of Scientific Imaging and Visualization[END_REF] While Latour [START_REF] Latour | Science in Action: How to Follow Scientists and Engineers Through Society[END_REF], Lynch and Woolgar [START_REF] Lynch | Representation in Scientific Practice, First Edition[END_REF] stress how images tend to objectify knowledge and to produce yet another argument in the rhetoric of science, we follow those who note the fecundity of images with respect to creativity and invention. Images in this respect are considered as epistemic creations [START_REF] Cambrosio | Ehrlich's 'Beautiful Pictures' and the Controversial Beginnings of Immunological Imagery[END_REF].

While the theme has been thoroughly studied in different fields of science, we think that it needs further analysis for at least two reasons. First we need to identify the diversity of poetic productions -not only images of science, but a whole gamut of other productions like names and logos. Second, we need to question their relationships and their impact on the project.

3

Case studies

Methodology

To study the poetic stance of inventors, designers and engineers we combined two methodological traditions. The authors participated and observed research projects from their inception to their completion and could therefore study the way names and narratives played a role in the maturation of the invention. These observations were eventually completed by in-depth interviews. These methods from social sciences, such as ethnographic observations and participations as well as interviews were complemented by methods from the humanities: literary analysis of texts, semiotic study of documents, analysis of media. These analyses focus on meaning-making as defined in semiotics [START_REF] Eco | The Open Work[END_REF], anthropology of material culture [START_REF] Keane | Semiotics and the Social Analysis of Material Things[END_REF] and more recently design studies [START_REF] Krippendorff | The Semantic Turn, A New Foundation for Design[END_REF]. They study the references to certain literary or visual traditions that are used within videos, texts, photos. These analyses show how words and tales not only can describe the technology, but also carry the imagination of the technology rather than its facts, and in doing so influence the way researchers look at their projects. Several aspects of this poetic process are exposed in this article: the question of naming the technologies in the view that they will become common names; the christening of projects with proper names, and the role of logos.

We defined a corpus of texts related to research projects that either author was taking part in. Semiotics scholars define a corpus as a group of texts (linguistic and visual) that belong to the same genre [START_REF] Lynch | Representation in Scientific Practice, First Edition[END_REF]. Texts of the same genre share structural and pragmatic characteristics in so far as they are not only similar in style, narrative structure, or visual organization, but also as they are discussed by social actors as targeting the same audiences, for the same purposes, with similar textual strategies. The constitution of the corpus does not pretend to be exhaustive. It is always but a segment of the actual production. The purpose is to gather enough material to answer a research question. The method therefore consists in defining a corpus of textual elements and considering what are the shared elements and their purpose in the text. In this particular instance, the authors selected only part of the poetic elements of the research practice". They focused the semiotic analysis on condensed forms of identity of the technology, of the group, through naming or representing by a logo over a diversity of situations: naming a field of research, a device, a communication norm. The purpose is to emphasize the similarities over different examples rather than to focus on the differences that should be further explored.

Scientific Puns: Play on Words and Definitions

In 1982, the three volumes AI Handbook attempted to present all of published AI projects of the time [START_REF] Barr | The Handbook of Artificial Intelligence[END_REF]. At that time the corpus consisted of 50 or so named research systems 1 . They were named with words or acronyms that punctuate the index with a variety of poetic heritages. WHY is a question answering system, BUGGY is a system to help students debug their understanding; some 30 such as WHY, BUGGY or Scholar use functional and denotative names. 20 more of the research project names such as CRYSTALIS or MYCIN are clearly connotative whimsical names words to make them memorable or celebrate some joke that have no bearing on the function of the programs. 8 of the names such as SOPHIE could be easily given a person. As this simple retrospective of the AI handbook shows, first there is a variety of naming options, and second many of them are visibly not pushed by purely descriptive purposes.

More generally, some words like 801.11 describe the way the technology came into being. Some words like "wi-fi" for supposedly "wireless fidelity" are the result of brand work. Others like " mesh networks" rely on metaphors that seems to relate to a physical network. Still other names completely drop out from the technical description to refer to an apparently arbitrary choice as the term "Bluetooth". In, this article, the central methodology follows the ethnographic approach of identifying concepts from an analysis of specific example projects. Here we study three examples that seem to belong to different naming strategies: "Bluetooth" as a private joke within a community, "Artificial Intelligence" as relating a technology to artificial beings in literature, "TrackPoint" as renaming in a lineage of products.

Bluetooth. Bluetooth as a name could have come from many places around the time of its creation. Blue lasers and LED's were the high-tech discoveries that were being 1 The 48 prominent AI research projects of the time are described in the1983 AI handbook [START_REF] Barr | The Handbook of Artificial Intelligence[END_REF]. The names of these research projects can be classified as, A: a person's name, B: connotative/whimsical or arbitrary, or C: functional. A. [START_REF] Barthes | Elements of Semiology[END_REF] used to sterilize things, to drive higher bandwidth on laser disks and optical fibers. But none of these references had the least to do with the short range Bluetooth radio standard. The term "Bluetooth" came out of meetings between researchers who were attending normalization meetings on the issue of short-range radio frequency. A leading actor of the "Bluetooth" norm, Jim Kardach, posted on his blog that the diversity of technical names was such that it led to use a code name from the culture of some of the participants [23]. The name King Harold Bluetooth of Denmark, who succeeded at unifying the various Danish regions in the 10th century, had emerged in discussions and post conferences. "Bluetooth was borrowed from the 10th century, second King of Denmark, King Harald Bluetooth (King Harald I of Denmark) who was famous for uniting Scandinavia just as we (Intel, Ericsson, and Nokia) intended to unite the PC and cellular industries with a short-range wireless link." [START_REF] Krippendorff | The Semantic Turn, A New Foundation for Design[END_REF] Rather than keeping complicated names or acronyms that were far too descriptive and technical the participants chose the name Bluetooth that started as a joke and finished as the expression for the norm.... "…it became apparent the need to have a single name; as Intel would talk to people about "Biz-RF", Ericsson about "MC-Link", and Nokia about "Low Power-RF", which also created confusion…" [START_REF] Krippendorff | The Semantic Turn, A New Foundation for Design[END_REF] While it was not meant to be the final name, it ultimately ended up becoming the official name of the standard.

TrackPoint. Another example is the "TrackPoint" device. According to its inventor, Ted Selker, it was the subject of multiple debates. At IBM, several years were spent developing a way of moving a cursor without moving hands from the home keyboard position. The project was first called the "Jstick" for the use of the right index finger on the j key as a joystick. Some people picked up this solution, included it in a few laptop computers in the mid 1990's, and marketed it as "Jmouse".

Due to problems with discoverability, installation, button overloading and handedness, the device moved out from under a key to between the g and h, and got christened with a functional name: "pointing stick" in press release and presented at Inter-act1990 [24]. Various people proposed that if a 6 inch handle that users wiggled was named a joystick this little one could be named "joy nub". As IBM's design consultant Richard Sapper chose red for the device, many thought of calling it a clitoris or a nipple. Scientific naming conversations focused on an easy way to remember the descriptive "objective" name, but product naming conversations focused on a descriptive "subjective" name. In the objective case, its origin in some early experiments under the j had people calling it the "Jstick", the underlying technical system being an isometric joystick and its use as a moving a cursor or focusing on a point on the screen got it called a "pointing stick". In the subjective case, people described the appearance and the gesture and considered it as obviously erotic. As the device was about to go to product, the naming department of IBM gave it a different name which turned out to have a trademark problem after the launch team was already being trained. There were tense conversations with the naming person who thought naming this little feature was trivial as he had whole product lines and products to name… With only weeks till public communications were to begin, he chose a recycled name: TrackPointII. The name TrackPoint had been trademarked as a descriptive name for a mouse product that IBM made in 1991 [START_REF]Trackpoint (Model L40 SX) Manual[END_REF] that could be turned over to be used as a trackball that a user rolled their hand over to move the cursor. The term track also changed the connotation: from a person's actions of rolling a ball to the person's goal of tracking down a point or pointing at the screen. In both devices, the "point" concentrates on the effect on the screen. Artificial Intelligence. Finally, in interviews with Selker, the late Professor John McCarthy explained how he picked up the name for a new field: "Artificial Intelligence" in the 1950s [START_REF] Mccarthy | A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence[END_REF]. McCarthy was a huge fan of science fiction throughout his life. In many conversations he would state that he had thought Artificial Intelligence (AI) would captivate people's imagination more than the other term he had been considering: "Machine Intelligence". Embedded in his choice of name was his love of science fiction but also the fact that he supposed that people would be more interested in exploring human intelligence as well as more capable machines and he wanted to connect the two. Instead of simply describing the work as making yet another engineering field, he liked the Artificial Intelligence expression for its poke at anthropomorphizing the quest.

Naming projects: acrobatic acronyms

Technologies that are invented need to be given a name. Similarly, though for a shorter period of time, research projects are also baptized. There too, one can witness a variety of inspirations: eventually project participants may forget what the name of the project stands for. One of the reasons is that they are made of acrobatic acronyms.

In fact, people often keep the acronym but change its explanation. Some names are more telling than the others: PLUG 2 (Play Ubiquitous Games and Play More) that was chosen for an RFID mobile game sounded both relevant and evocative. "Transhumance 3 ", "Safari 4 " that focused on adhoc networks, were suggestive of mobility, but did not give any real clue to how they were dealing with it. And of course, "Adam 5 " or "Popeye" 6 are particularly obscure.Gentes could observe the naming process for a research project carried out by two departments of social sciences (Center for the Sociology of Innovation in Ecole des Mines and the Co-design Lab at Ecole des Telecom) as well as a legal research lab (CERNA). The team wanted to study the uses of peer-to-peer but also mobility and ad hoc networks. They wanted to understand how these technologies were spreading in our society. In fact, a good part of the discussion was about the object of research itself. For a part of the team, peer-to-peer was a good starting point, but it focused on applications, while the other members wanted to focus on infrastructures. Finally, the common denominator between the two groups was the fact that they were interested in Distributed Architectures. That sealed at least 2 letters of the acronym that the team was looking for: AD. But Distributed Architectures is a much broader field than a project around communication could own. Starting with AD, they played on a number of combinations based on the sonority and the connotations of the words: AD + mobiles information: ADMIN that gave the project the connotation of computer management. The team tried: AD+ Evolution of Relations: ADER. But the group promptly remarked that it sounded like the inventor Clément Ader who was a major inventor in the aviation field and also for the telephone. AD + Ingénierie des Relations (engineering of relations): ADIR. But none of these names were "fun" and the group finally came up with ADAM that would stand for "Architectures Distribuées Pour Applications Multiples" (distributed architecture for multiple applications), that did not mean much more than the other options but that combined applications with infrastructure and a biblical name, knowing that at the time there was only one man in the team.

Scientific logos: a question of identity

The logo is a sign of branding permeating the logic of research as a commodification process [START_REF] Fishman | Manufacturing Desire: The Commodification of Female Sexual Dysfunction[END_REF] that "mediate the junctions between technological producers and consumers". Our view is that this trend of naming is not only functional, memorable and strategically positioning the project but also poetically adding to the identity of the project.

We would like to go back to one particularly successful example that was, in effect, realized by a professional designer, while most of the time new project teams and research groups try to find the resources within their team, students, or friends who agree to do it for free… The project is called ILHAIRE: Introducing Laughter in Human Avatar Interaction, Research and Experiment [START_REF] Mccarthy | A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence[END_REF]. The logo was realized by one of the research partners, the French animation company: La Cantoche. Though it is not a communication company and it does not do logos for a living, they have graphic designers and artists who could work on the project. The whole process is interesting in as much as it is quite different from an ordinary process of branding. According to the project manager, one of the main differences is that the team includes "a large variety of disciplines and interests in the project and there is no formal hierarchy between the members [interview with J.]" so making a decision was perhaps more complicated in that respect. Another difference is that the logo is defined in relation to the theme of the project (and not as could be the case in other circumstances to position the "product" within a market). What makes it really precious then is that, according to J., "it focuses on the identity of the project, the "heart" of the project".

The exchanges between the different participants showed that this identity was a subject of controversies: social scientists contended that laughter was social, for others it was important to see that laughter was necessary to health and well being. Engi-neers focused on the models of gestures and acoustic dimensions. The team was also worried that talking about laughter would not be taken seriously. They wanted to make it clear that it was research, which was finally why the baseline was chosen: "the science of laughter". The production of the logo was therefore one of the productive and possibly rare occasions where the different stakeholders explained and compared their viewpoints.

What makes it a real poetical work is that the participants had to learn about the process itself, the impact of images, the role of signs and words. The role of the project manager was particularly important in sharing this vision about the making of a logo. For instance, they had to understand that a logo is "not an illustration". In particular, it was tempting to use an avatar as part of the logo but then it would work as an example, rather than a sign with a more general purpose. They also had to work on how to share values through the choice of a logo: "warm colors", "playful", or showing: "the collaboration between different fields of research (engineering + human sciences) [28]".

The process went from a culture of words with denotative value to a process of images with an emphasis on culture-from trying to deploy the acronym, to gradually incorporating the culture of cartoon, with references to the field of computer science.

The final logo shows a mastery of graphical techniques: see for example, the bubble and emoticon that are left open by the frame of the rectangles. But more importantly, it played a role in the representation of the project for the participants. It crystalized a partnership, an identity, and the representation of a technology.

Findings

Despite the differences between giving names to technologies, to projects or designing a logo, the examples in this paper emphasize the relationship between a technology and the different ways to invent it.

Names are caught between a denotative and connotative tension. In the naming process, the example of the TrackPoint naming story as told above is an epic journey of descriptive functional names (pointing stick) and descriptive joking names with sexual innuendos. Despite the differences, the top three options ("pointing stick", "joy nub", "TrackPoint") struggled with an accurate description of the device. The name either points to the underlying technology or to its surface properties, how it works or how it is perceived from a user perspective. The difficulty to choose between denotative options: pointing stick and TrackPoint, shows that to describe something might not be so easy in the first place. As demonstrated by Bucciareli [START_REF] Bucciarelli | Designing Engineers[END_REF], designing is about agreeing on a definition; different stakeholders have a different definition of what the technology is about. What this example as well as the Bluetooth example show is that the denotative choice is not solving the problem and that names are produced also with connotations in mind. This is obvious in both "Artificial Intelligence" and "Bluetooth" as well as the discarded "joy nub". Connotations deepen the cultural relationship of the technology with other artificial products because they evoke linguistic and social codes, rhetorical and ideological conventions and therefore enlarge the cultural references of the innovation. Play on words are used to explore and exploit connotations and support associations of ideas in the case of naming technologies or projects. As related by its inventor, "joy nub" was probably the more striking and memorable name, but was abandoned for reasons of propriety. Artificial Intelligence was picked by its author with a background of at least two centuries of literature on artificial creatures. What was at stake was to stimulate the imagination and the research thanks to the evocative metaphor of intelligence and the open ended nature of the word artificial that no longer related the expression to an actual artifact but acted as far more diffused.

Names are also compared to other names and finding the right name might have to do with the necessity to break from or on the contrary to establish a connection with other names. Trackpoint (at the beginning called Trackpoint II) was chosen to establish such a connection. It was thought of in relation to other existing products or devices. The naming process therefore focused on founding the new device within a family, and gave it a sense of continuity, an inscription in the history of tracking devices. Part of the imaginative process was about the lineage of technical artifacts. On the other hand, Bluetooth does not relate easily to any other existing technology and is strikingly original as it decidedly departs from any denotative and descriptive naming tradition.

Giving a name to a project seems to be slightly different from giving a name to a technology. While the inventors of a technology aim at creating a common name, the groups who baptize their project inscribe their activity in history and identity through a proper name. Names given to project can be chosen to be descriptive, to remind people of the roots of or aspirations for the project, or simply be a memorable word or sound that will attach to people minds to help the project. According to Kripke [START_REF] Kripke | Naming and Necessity[END_REF] this proper name stands for the dubbing ceremony that accompanies its creation. As proper names, they have some relationships with the culture of their authors: Bluetooth is chosen by people mostly coming from Nordic countries, Adam is a private joke about the only man of the research team. The connotations are then exploited to signify not so much the technology as the collaborative work of the team. But the word then does not so much refer to anything (who really knows who Bluetooth was and what he did?) than build on the originality of the choice of words that appear truly poetic if not totally random: a tooth: something tiny but piercing, associated with the unlikely colour blue that might refer to a range of other technologies of the time (Blue Ray for instance). While proper names are the subject of debates between descriptive or causal historical theories [START_REF] Brock | The Ubiquitous Problem of Empty Name[END_REF], an important point is that they are also contextsensitive [START_REF] Burge | Reference and Proper Name[END_REF]. This means that they can take on meanings very differently according to the context of utterance. In fact, a proper name invites different viewpoints on the same thing because it is not purely descriptive: for each partner of the ADAM project, the identity of the project remains open to interpretation. In fact, the combination of Distributed Architecture on the one hand, and Multiple Applications on the other while originally chosen for evocation purposes, also forced the members of the team to think about the relationship between infrastructures, applications and services. It expanded the conceptualization of these systems. As a proper name, they benefit from a certain "looseness", as defined by Searle [START_REF] Searle | Proper Names[END_REF]. They do not describe and give attributes to an object (or person) but still refer to it in ways that open to a whole family of possible descriptions. A literary analysis of these names shows that they try to mix technical features with a symbolic representation of the technology.

Designing logos apparently shares some of these issues of group identities and technological descriptions as the example of Ilhaire tends to show. The image is chosen to represent the technology without using the usual tools of the trade, like scientific schemas. Rather the example shows that several cultural relationships are designed within the iconic choices. The logo is clearly related to the culture of cartoons. It also alludes to emoticons and finally to scientific images through the representation of a sound curve. It functions as a metonymy: the project is part of a wider scientific, cultural and literary whole. Similarly to the expression "Artificial Intelligence", it therefore opens up to larger realm of possible research and applications.

Conclusion

In this article, examples bring forward the value of imagination and strive to promote the study of poetic practices in research project. These examples show that we can use methods that combine sociological methods and methods from humanities to produce thorough analyses and comparisons and come up with typologies of poetic practices integral to all IT project. The primary function of these writing practices is not only descriptive. Names of technologies, acronyms for projects, logos and narratives, are motivating, aspirational and evocative practices that support the expansion of the scope of the technology.

Poetics can be understood in production and in reception. In production, the poetic work is an aesthetic and intellectual work that looks for meaning. These productions, rather than working on precise attributes, operate what the logician and philosopher of language Saul Kripke, calls the "opening of possible worlds". Even in the case of technology naming that tends to limit the possible connotations, we have seen that the exploration of names becomes an expansive part of a project with results hovering between denotation of what the project is about and connotations of how people want to feel about it. In reception, the proper name of a project is also poetic, because like other proper names, it supports a context-sensitive diversity of interpretations. The Artificial Intelligence example above shows how the diversity of interpretations might be designed into a name to help the project to continually expand its goals. Poetics as part of the aesthetic experience depends on the process of open interpretation. A word or an image that seems to close the object on its properties does not support the same interpretive leeway. We can use the difference that Umberto Eco [START_REF] Cambrosio | Ehrlich's 'Beautiful Pictures' and the Controversial Beginnings of Immunological Imagery[END_REF] analyzes between a traffic sign and a work of art. The traffic sign is no longer a traffic sign if it is freely interpreted and discussed. But a work of art constitutes "an open product on account of its susceptibility to countless different interpretations which do not impinge on its unadulterable specificity. Every reception of a work of art is both an interpretation and a performance of it, because in every reception the work takes on a fresh perspective for itself [START_REF] Cambrosio | Ehrlich's 'Beautiful Pictures' and the Controversial Beginnings of Immunological Imagery[END_REF]. Therefore, what is at stake in these poetical practices of engineering is to explore the cultural meanings of the technology, then to open it to a diversity of interpretations.

This paper focuses on the semiotics of condensed forms of identity through name and logo. But poetic practices include other literary and visual forms that can affect research projects. In particular, we can concentrate on narratives such as use-cases or personas and the way they contribute to build and expand the scope of research projects. We could also compare these findings to research projects over time. Placing this research within a historical perspective would help us to consider different poetic practices, different genres and styles over time. For instance they would help to figure if the growing multidisciplinarity in research projects is a key factor in the production of figures of speech that gather the different disciplines. In further research, we can also explore how words and images that accompany the emergence of a technology not only help researchers anchor their technology in culture, but also contribute in special ways to the involvement of potential users. In particular, when users are asked to test technologies, how do they interpret them based on a diversity of factors including their names and other representations. We should further enquire into how these representations help structure a design for debate around its value and use [34]. This research program can help to establish a taxonomy of poetic practices according to fields of research, actors, types of research products. Beyond the taxonomy, a theory of a poetic of research can help the process of invention by articulating findings to an artifact situated in and incorporating social and cultural context.

  of the 48 names could easily be used as a person's name without notice: DEDALUS, GUIDON, HAM, NOAH, PECOS, SOPHIE, TEIRESAIS, WUMPUS. B. 20 of the names are connotative whimsical or arbitrary: ACRONYM, CRYSTALIS, CASNET, DEDALUS, GUIDON, HAM, HARPY, IRIS, LIBRA, MYCIN, MEMOD, MOLGEN, NOAH, PECOS, SAFE, SOPHIE, STRIPS TEIRESAIS, WEST, WUMPUS. C. 28 of the projects are named with functional or denotative names: ABSTRIPS, ACT, Alpha beta, AM, AQ11, BUGGY, CHI, CONGEN, DENDRAL, EPAM, EXCHECK, EXPERT, HACKER, HEARSAY, HWIM, INTERLISP, LEX, Meta-DENDRAL, MACYMA, Minimax, NLPQ, PROSPECTOR, Protosystem, PSI, SCHOLAR, Student, The Programmers Apprentice, WHY.

PLUG -play ubiquitous games-ANR/RIAM -2007-2009.

Transhumance (Ad Hoc Services: Fulfilling a Platform for Peer to Peer Applications on Mobile Ad Hoc).

[START_REF] Simondon | L'invention dans les techniques[END_REF] Safari -Mobile Ad Hoc Networks -ANR/RNRT-2003-

2005.[START_REF]Aristotle: Poetics[END_REF] Architectures Distribuées Pour Applications Multiples , http://adam.

hypotheses.org/ 6 POPEYE (Professional Peer Environment Beyond Edge Computing) -STREP/IST -2006-2008.


Yanan Li

Yong Wang

Social Influence from Personalized Recommendations to Trusting Beliefs of Websites: Intermediate Role of Social Presence

Keywords: Personalized Recommendation, Personalization, Social Presence, Trusting beliefs

With the increasing prevalence of online shopping, many companies have added "personalized recommendation" modules on their websites to collect customers' click-stream data in real time and conduct relevant analysis, which will both assist the decision-making of the web owners and enrich the interactive experience of the customers. A website with more personalized recommender system allows users to experience others as psychologically present being, conveys a feeling of human warmth and sociability which is believed critical for the formation of consumers' trust towards online stores. In this study, a laboratory experiment was conducted to empirically examine the effects of the level of perceived personalization on consumers' trusting beliefs towards online store. The result also demonstrated that the perception of social presence can partially mediate the effect of perceived personalization on consumer's trusting beliefs.

Introduction

Web interface, the bridge between websites and consumers, not only undertakes the practical task, but also works as an emotional bond, bringing unique experience to form a social connection between websites and customers. Websites with personalized recommender system, with Amazon.com as the representative, learn consumers' preferences based on the click-stream data, so as to achieve a personalized presentation. Personalized recommending helps consumers to screen items they might be interested in from numerous commodities, and reduces information overload and increases choice efficiency. At the same time, it realizes the goal of persuading consumers by influencing their choice and preference [START_REF] Liang | Personalized Services as Empathic Responses: the Role of intimacy[END_REF][START_REF] Tam | Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes[END_REF]. Apart from the practical functions mentioned above, the scenario of a one-on-one conversation is created between consumer and website during the recommending process, thereby generating a feeling that he/she is "being with other people". This is the foundation for the forming of the perception of social presence. Many investigations show that social presence is a key factor influencing the consumer trust towards the website [START_REF] Gefen | Consumer Trust in B2C E-Commerce and the Importance of Social Presence: Experiments in E-Products and E-Services[END_REF][START_REF] Hess | Designing Interfaces with Social Presence: Using Vividness and Extraversion to Create Social Recommendation Agents[END_REF]. Previous studies, however, rarely discuss how it is affected by personalized recommendations. This study aims to investigate the effect of personalized recommendation on the consumers' trusting beliefs towards the website and the role played by the perception of social presence in this process.

2

Literature Review

Personalized Recommendation

Personalized recommendation is a representative of personalized service [START_REF] Tam | Web Personalization As A Persuasion Strategy: An Elaboration Likelihood Model Perspective[END_REF]. Most early recommendations are generated by asking about customers' preferred commodity attributes in advance. As the recommendation algorithm keeps improving and data mining technology is making advances, personalized recommender system as represented by Amazon.com can achieve real-time indirect conjecture on consumer demand based on click-stream data. Compared with earlier strategy, this contextbased recommendation strategy moves closer towards personalization. A personalized recommender system helps in alleviating information overload, improving choice efficiency, and enabling cross-and up-selling. Besides, it also influence consumers' cognitive processing and decision-making [START_REF] Xiao | E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact[END_REF], their evaluation [START_REF] Kumar | The Influence of Recommendations and Consumer Reviews on Evaluations of Websites[END_REF] and satisfaction, loyalty and trust to the website.

Researchers have been devoting to increase the accuracy of recommending algorithm. The bottleneck in this process is mainly associated with the constructive nature of consumers' preference. While evaluating personalization level, there is no baseline website to compare against, regardless of the recommendation method used. Consumers' perception of personalization varies depending on the tasks, context and time. So personalization is not a technical term, but a psychological variable. According to psychological theory, the information more relevant to the consumers' goal will result in more perceived personalization and deeper cognitive processing, which is the essence of web personalization [START_REF] Kumar | The Influence of Recommendations and Consumer Reviews on Evaluations of Websites[END_REF].

Perception of Social Presence

The notion of social presence refers to that, in the process of communication, the media of communication gives the individual a social feeling that he/she is "being with other people". In its nature, the perception of social presence is closely related to two psychological concepts: Intimacy and Immediacy.

In earlier studies, the factors identified as triggering the perception of social presence include emotional texts, images, personalized greetings, as well as information technology(IT)-enabled support for the provision of live chats, forums, consumer reviews, live help, and virtual reality technology, etc. It has been revealed that the perception of social presence causes more positive assessment on the efficacy of website, the satisfaction, pleasure experience and intention to use [START_REF] Choi | The Influence of Social Presence on Evaluating Personalized Recommender Systems[END_REF]. However, few researchers investigate the effect of perceived personalization on the perception of social presence [START_REF] Choi | The Influence of Social Presence on Evaluating Personalized Recommender Systems[END_REF].

Trusting beliefs towards Websites

Unlike traditional context, shopping online seems to be deficient in human touch. It is more difficult but more important to build consumer's trust towards online store.

McKnight, Choudhury and Kacmar [START_REF] Mcknight | The Impact of Initial Consumer Trust on Intentions to Transact with A Web Site: A Trust Building Model[END_REF] believe that, in the context of online shopping, the trust towards a website consists of trusting beliefs and trusting intention. Trusting beliefs, reflecting consumer's concrete cognition and emotional belief towards the can directly lead to trust intention and trust-related behavior. In previous studies on user adoption of personalized recommendation, trust is a key psychological variable. Personalized recommendations can facilitate the formation of trust by improving the transparency, perceived control and internalization.

Some studies suggest that personalization can result in emotional sense or affective experience, like feelings of intimacy, perception of social presence [START_REF] Liang | Personalized Services as Empathic Responses: the Role of intimacy[END_REF], which can improve trusting beliefs towards online store. Therefore, it is a new trend of research to put emphasis on the emotional bond between the consumer and website, and on the formation of trust from the social aspect.

Hypothesis Development

Kumar and Benbasat [START_REF] Kumar | The Influence of Recommendations and Consumer Reviews on Evaluations of Websites[END_REF] are among the first to have discussed the difference between personalized and non-personalized websites in creating users' perception of social presence. They filtered Amazon.com's contents in real time in laboratory experiment, and found that the personalized recommender service in Amazon.com improves its social presence. The study conducted by Choi et al [START_REF] Choi | The Influence of Social Presence on Evaluating Personalized Recommender Systems[END_REF] manipulated the recommendation pages with varying levels of personalization according to the connections between the recommendation source and the consumer, in order to explore the influence of products recommendations with different personalization levels on the perception of social presence and intention to use. The results indicated that, compared with non-personalized recommendation (for example, ranking lists), users experienced a higher level of social presence in personalized recommendation page. We believe that the higher the personalization level perceived by consumers from a website, the more likely the consumers feel involved in a one-to-one dialogue between him/her and the website. The intimacy and sociability easily create a warm feeling, i.e. the perception of social presence. Hence we make the following hypotheses:

Hypothesis 1: Recommendation webpages with higher personalization level strengthens the consumer's perception of social presence.

The content, presentation, and timing affect and what the consumer feels about the personalization level of the recommendation, influence his/her judgments on the website. Previous studies mostly focus on the recommendation agencies which acquired information of user preference explicitly. It shows that consumers develop higher cognitive trust and emotional trust towards the website when their perception of personalization is higher [START_REF] Komiak | The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents[END_REF]. Moreover, the stronger the perception of personalization, the more precise, in the consumer's point of view, the website's conjecture on his/her preferences would be, which enhances his/her trusting beliefs towards the website. Hence, Hypothesis 2: Recommendation webpages with higher personalization level provoke stronger trusting beliefs from the consumers.

Through previous studies, it is not difficult to conclude that the perception of social presence is a key intervening variable in the process of users reading through the web interface elements to affect the user trust [START_REF] Gefen | Consumer Trust in B2C E-Commerce and the Importance of Social Presence: Experiments in E-Products and E-Services[END_REF][START_REF] Hess | Designing Interfaces with Social Presence: Using Vividness and Extraversion to Create Social Recommendation Agents[END_REF]. In this study, further test is made to see whether user's perception of social presence will mediate the influence of perceived personalization on trusting beliefs towards the website. Hence, Hypothesis 3: The perception of social presence mediates the influence of personalization on trusting beliefs.

Research Methodology

The task for the experiment consisted of asking participants to imagine that he/she is going to do some information searching for what he/she needs to buy, then browse web pages of a prototype shopping website named "Orangeebuy.com". The study was designed as a one-factorial experiment manipulating two levels of perceived personalized in two independent groups. Participants were randomly assigned to the two groups, where each participant was exposed to only one level of stimulus or condition.

The prototype site was designed in terms of content and page layout to resemble typical e-commerce sites, such as Amazon.com. Product introduction page displayed basic product information of one item, followed by a set of recommendations-8 similar products-preceded by the words "products recommended". For each condition, three product introduction pages involving three items, namely, headphone, TOEFL book and walnut, which were chosen in earlier studies [START_REF] Gefen | Consumer Trust in B2C E-Commerce and the Importance of Social Presence: Experiments in E-Products and E-Services[END_REF][START_REF] Choi | The Influence of Social Presence on Evaluating Personalized Recommender Systems[END_REF], were displayed. In order to confirm that the three items were within the scope of online shopping for college students in Beijing, an online questionnaire survey of about 200 undergraduates in Beijing was conducted. The results showed that books, digital devices and snack food were the top three product categories they most frequently purchased, and we also identified the commodity properties of the three items (price, brand, etc.) that the students were mostly concerned about when purchasing online.

All conditions, featured the same information of product introduction, but differed only in terms of recommendations. Following the strategy of "goal-relevance", which is considered as the major feature of e-personalization [START_REF] Tam | Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes[END_REF], participants in Group High , received more relevance recommendations to the product being introduced on current page based on the commodity property identified before than those in Group Low .

Following the completion of the task in the main experiment, all items were statements and subject responses were offered on a 7-point Likert scale ranging from Strongly Agree(1) to strongly disagree [START_REF] Kumar | The Influence of Recommendations and Consumer Reviews on Evaluations of Websites[END_REF] . All the measuring scales were previously validated, including scale for Trusting beliefs [START_REF] Hess | Designing Interfaces with Social Presence: Using Vividness and Extraversion to Create Social Recommendation Agents[END_REF][START_REF] Mcknight | The Impact of Initial Consumer Trust on Intentions to Transact with A Web Site: A Trust Building Model[END_REF], scale for perceived personalization [START_REF] Komiak | The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents[END_REF], and scale for social presence [START_REF] Gefen | Consumer Trust in B2C E-Commerce and the Importance of Social Presence: Experiments in E-Products and E-Services[END_REF] and measuring for demographical profiles. Each participant would get a reward of 10 RMB and some candies.

Data Analysis

A total of 114 undergraduates successfully completed the main experiment. Among them, 55 are arranged in Group High and 59 in Group Low . All participants were recruited from universities in Beijing, China, including 39 males and 75 females with an average age of 23.5 years old. All participants reported that they had online shopping experience.

An independent-samples' t-test shows that the average score of perceived personalization between Group High and Group Low is significantly different (Mean Low =4.76, Mean High =5.65, t=5.172, p<0.001), which means that the experimental manipulation is successful.

We perform confirmatory factor analysis using AMOS 18.0 on the measurement model to assess internal consistency reliability, convergent validity, and discriminant validity. Trusting beliefs are represented as a second-order reflective construct, with benevolence, competence, and integrity beliefs represented as first-order reflective constructs [START_REF] Mcknight | The Impact of Initial Consumer Trust on Intentions to Transact with A Web Site: A Trust Building Model[END_REF]. Following the procedures recommended by Gorsuch, a first-order confirmatory factor analysis is used to confirm the structure of the three dimensions of trusting beliefs. The result shows, χ 2 / df = 1.782, NNFI = 0.906, CFI = 0.955, RMSEA = 0.083; the correlation coefficient of three sub-dimensions is 0.4~0.7 (0.65, 0.65, 0.48). In second-order analysis, the model fitting indexes are also acceptable. But the correlations between second-and first-order factors are stronger (0.66, 0.67, 0.96). Therefore, Trusting Beliefs as a second-order reflective construct can sufficiently express the concept connotation with a convenience of model simplification by averaging items from three sub-dimensions scales to represent the Trusting Beliefs.

A reliability test is conducted with an internal consistency test (Cronbach's alpha). The measurement items used in this study are shown to be reliable in that Cronbach's alpha values for all dimensions are at least 0.7. Then, the convergent and discriminant validity of the constructs are assessed and suggested to be acceptable as described in Table 1 and2. To test the hypotheses, we test the perception of social presence and trusting beliefs between the two groups using One-way ANOVA. It is found that significant difference exists (Social Presence: Mean Low =3.97, SD Low =0.96, Mean High =4.68, SD High =1.19, F social presence =11.923, sig. = .001; Trusting Beliefs: Mean Low =4.49, SD Low =1.02, Mean High =5.12, SD High =0.71, F trusting beliefs =13.992, sig. = .000). Thus, Hypothesis 1 and Hypothesis 2 are confirmed. That is, recommendation webpages with higher personalization level can create a stronger perception of social presence and trusting belief.

In all of the regression analyses that follow, we first enter a series of control variables before testing Hypothesis 3. These control variables include gender, age, education, hours spent on Internet per day, and shopping frequency in recent three months.

To test for mediation, we use the following four-step procedure outlined by Baron and Kenny (see Table 3): Model 1 shows that Perceived Personalization is significantly related to Social Presence (β=0.548, p<0.001). Model 2 suggests that Perceived Personalization is significantly related to Trusting beliefs (β=0.608, p<0.001). Model 3 shows that Social Presence is significantly related to Trusting beliefs (β=0.502, p<0.001). Support for the mediation hypothesis would be observed if the initially significant relationships we find between Perceived Personalization and Trusting beliefs disappear or decrease after we add Social Presence into the regression equation. Model 4 indicates that after Social Presence is added to the regression model, the initially significant relationship between Perceived Personalization and Trusting beliefs does not disappear (β=0.440, p<0.001). This result suggests that Social Presence only partially mediates the relationship between Perceived Personalization and Trusting beliefs. Thus, Hypothesis 3 is confirmed.

Discussion

The experimental results show that, the recommending pages with two different levels of personalization arouse different perceived social presence and trusting beliefs towards online store. The higher the level of personalization that the users perceive, the stronger the perception of social presence, and the greater the trust towards the online store the consumers would have. The mediating effect of social presence is significant for the design of recommendation system. Theoretical contributions of this study would be as follows. First, for users, personalization is a kind of psychological perception and evaluation of goal relevance. The greater the relevance to self needs, the higher the level of personalization he/she perceives. Second, personalized recommendations can directly affect trusting beliefs towards online store, and also affects indirectly by improving users' emotional experience. Third, perception of social presence is crucial for trust building process. Past researches mainly focus on improving the credibility of recommendation in order to enhance trust, while this study adds the social and affective influence of personalized recommending.

This study has several limitations. Our samples are composed only of undergraduate students. Future research should include participants within a more diverse range of age, online shopping experience and computer experience. And the personalized recommendation concerned in this paper is not a real one generated by a computer system. Therefore, the ecological validity of the experiment is affected.

Nonetheless, this study has already demonstrated the importance of personalized recommendation in terms of social impact. Managers who provide personalized recommendations on their online store should give concern to users' perception of social presence generated in the interaction with website. In addition, personalized recommendations might have different effects in combination with some social network elements and other environmental or psychological factors that might influence the social impact of personalized recommendations, which are all worthwhile for further investigation in the future.

Table 1 .

 1 Results of Reliability and Convergent Validity Analysis

	Items	Standardized	Cronbach's	Items	Standardized	Cronbach's
		loadings	alpha			loadings	alpha
	PP1	0.755	0.778		TBb1	0.709	0.808
	PP2	0.889			TBb2	0.803
	PP3	0.600			TBb3	0.790
	SP1	0.791	0.877		TBI1	0.875	0.915
	SP2	0.737			TBI2	0.903
	SP3	0.689			TBI3	0.879
	SP4	0.867
	SP5	0.784
	TBc1	0.773	0.851		Trusting Belief		n/a
					Dimension
	TBc2	0.826			Competence	0.884
	TBc3	0.843			Benevolence	0.753
	TBc4	0.661			Integrity	0.600
	χ 2 / df= 1.743 (df=129) CFI=0.923	NNFI=0.841 RMSEA=0.081

Table 2 .

 2 Average Variance Extracted (AVE) and Construct Correlations Square root of AVE in bold on the diagonal. The AVE for each construct was > .5 as recommended. The square root of the AVE for each construct was also > the correlations with other constructs as recommended.

		AVE	PP	SP	TB
	Perceived	0.797	0.892
	Personalization(PP)
	Social Presence(SP)	0.882	0.565	0.939
	Trusting Beliefs(TB)	0.949	0.930	0.811	0.974
	Note:

Table 3 .

 3 Hierarchical regressions for mediation tests (N=114)

	Variable		Model 1	Model 2	Model 3	Model 4
			Social	Trusting	Trusting	Trusting
			Presence	Beliefs	Beliefs	Beliefs
	Control
	Gender		-0.041	0.135	0.180	0.138
	Age		-2.09	-0.213	-0.054	-0.153
	Education		0.009	0.023	-0.049	0.022
	Hours on the internet	-0.065	-0.13	0.009	0.006
	per day
	Shopping Frequency	0.125	-0.05	-0.063	-0.084
	R 2		0.232	0.037	0.193	0.193
	Perceived personalization	0.548 ***	0.608 ***		0.440 ***
	R 2		0.529 ***	0.452 ***
	Social Presence			0.502 ***	0.311 ***
	R 2				0.624 ***	0.745 ***
	* p<0.05	** p<0.01	***p<.001

Acknowledgements

This study was partially supported by the Knowledge Innovation Project of the Chinese Academy of Sciences (No. KSCX2-EW-J-8, 2011~2013), and the Special Fund for Beijing Key Discipline Construction (Applied Psychology, 2008~2012).


Guilherme Coletto
email: guilherme.rotta@gmail.com

Vinícius Silva De Lemos

Ana Luiza

Moura Da Cunha

Isabel Harb Manssour
email: i-sabel.manssour@pucrs.br

Milene Selbach Silveira
email: milene.silveira@pucrs.br

André Fagundes Pase
email: afpase@pucrs.br

Exploring Twitter Interactions through Visualization Techniques: users impressions and new possibilities

Keywords: visualization techniques, social networks, twitter users' behavior

Social networking websites have been taking a huge space on the Internet. With millions of members spread across the world, these services connect their users through "friendship" relations, and within a short period of time the information is disseminated in the network. Considering Twitter, we investigated how standard techniques of information visualization can improve the analysis of its users' behavior, i.e. the group of people who most closely relate, and the frequency and type of messages they send. This investigation was done through user's studies with a set of visualization techniques implemented in an interactive system prototype.

Introduction

The advent of Web 2.0 has aroused the emersion of new services for the Internet over recent years, giving users a higher level ofuser-to-userinteraction in the network. Within this context, several applications focused on the relationship among users were developed, created to bring together people with specific interests in common, to facilitate the contact between distant friends and, for instance, the contact between a company and its customers. Social networks are, mostly, websites created with these purposes, and have millions of members spread around the world [START_REF] Boyd | Social Network Sites: definition, history, and scholarship[END_REF]. With the everincreasing number of members, a large amount of data is created on these networks and flows over the Internet. Making an analysis of this information can be difficult, since the complexity and the way it is organized do not assist in this task.

Taking into account the amount of information published in these networks, and the growing speed in which this information is updated and exchanged, the research herein presented focuses on how standard and interactive techniques of information visualization can help in the relation and extraction of new information based on what has already been published.

Considering Twitter, it differs from other social networking sites because a friendship link doesn't need to be accepted by both parts: in order to receive tweets from a user, a person must follow him/her; however, the user is not obliged to follow this person back. Since Twitter just allows you to view the friends of one member at a time, and through a simple list that appears on every profile page, information visualization techniques can help to better view the network of contacts, and to analyze the frequency that individuals or groups send tweets and what kind of tweets they sendthe Twitter Users' Behavior.

In order to investigate this, we made user studies using a system prototype developed in our University, which provides visualization techniques that allow the analysis of users' connections, and the frequency of tweets sent by one or a group of users, and, mainly, it provides ways to classify these tweets, a feature difficult to find in most existing visualization tools for Twitter. Thus, as our main contribution, we demonstrate how interesting for the user point of view (given different user profiles) is to allow an integrated manner to explore and analyze the interactions of one user or a group of users in a social network.

This next section presents the visualization techniques available in the prototype. The user studies and a discussion on how they can help to analyze users' behavior are following presented. Some conclusions and future works are presented in the last section.

Visualization Techniques to Analyze Users' Behavior

There are many tools that use information visualization techniques to help in the analysis of users' behavior, as presented in some previous works [START_REF] Barata | AppInsight: What have I been doing?[END_REF][START_REF] Leung | Exploring Social Networks: a frequent pattern visualization approach[END_REF][START_REF] Marcus | TwitInfo: aggregating and visualizing microblogs for event exploration[END_REF][START_REF] Kaye | Nokia Internet Pulse: a long term deployment and iteration of a Twitter visualization[END_REF]. In the work herein presented, we used the prototype system developed by Rotta et al. [START_REF] Rotta | Visualization Techniques for the Analysis of Twitter Users' Behavior[END_REF], called DeepTwitter.

DeepTwitter provides standard visualization techniques, selected according to some related work [START_REF] Heer | Visualizing online social networks[END_REF][START_REF] Perer | Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis[END_REF][START_REF] Kwak | Visualizing a Personal Timeline By Adding Multiple Social Contexts[END_REF], to allow the understanding of how they can help in the users' behavior research in social networks. Through them, users' connections and the frequency of tweets sent by one or a group of users can be easily analyzed. Tag cloud visualization, most popular users and ways to classify the tweets are also provided, enabling the learning about the social network and the behavior of its members, through a constant collection of generated data.

Figure 1 shows DeepTwitter's main window, which displays the graph of the user's social network, expanded as he/she selects members of the network to include their friends or followers to the network. Two groups of users can be also observed in Figure 1, as well as a window that quickly shows some information regarding the user on which the mouse is over, such as his/her last tweet, description, number of friends and followers, etc. This visualization plays an important role, given that it has the ability to display, at once, a large number of members and connections, a task not possible to be done through the website. The discovery of other members that the user might have interest in also occurs in a very efficient way, through the verification of which users related to him are already following these members, for example.

The prototype also enables the creation of categories, into which different tags can be inserted. Through these tags tweets could be classified in both, timeline and tag cloud visualizations. Thus, it is possible to differentiate tweets by subject, clustering all the tweets that contain a given set of tags inside the same category that holds these tags. When visualizing tweets from individuals or groups in DeepTwitter's main window it is possible to open the timeline visualization. The timeline organizes the tweets on a chart, based on the day (x axis) and time (y axis) in which they were sent, allowing the observation of a user or group of users behavior, such as the frequency in which the tweets are sent and what times and subjects are predominantly devoted to it. For timeline visualization, users can choose: the option to display the users' pictures or ellipses for each tweet; and the option to classify or not tweets within the categories already defined. By doing this, the pictures are outlined or the ellipses are filled with different colors, in order to facilitate the identification of which tweets belong to each category. Moreover, the tags are highlighted when the mouse moves across the tweet, as shown in Figure 2.

DeepTwitter also provides a tag cloud visualization, which shows the tags with different font sizes to allow the user to quickly and easily find information [START_REF] Halvey | An assessment of tag presentation techniques[END_REF]. The tags could be the ones specified by the user when creating categories, or the ones that are currently the Trending Topics at the Twitter homepage. Finally, the most popular user visualization can be applied to a group of selected members or to all users in the contact network. The classification can be made by the number of friends, followers, favorite tweets, or tweets that have already been written. When one of these options is selected through a button interface, the picture size is appropriately changed in relation to the selected option and to other users. According to the selected option, the user that has more friends, e.g., appears with the biggest picture, and the one that has fewer friends, appears with the smallest picture. Through this visualization, with just a quick look the user can identify the most active or important members from his network of contacts, which would be very difficult to do using the Twitter website.

Getting User Impressions about the Visualization Techniques

In order to verify users' impressions about the use of visualization techniques, and to know which public would be more interested in this kind of tool, an user study was conducted, in two phases: the first one focusing on the general public, characterized by people who use Twitter for personal use (without professional goals), and the second focused on professionals who use Twitter as support for their work or even as their main activity (as professionals who monitor the impact of their companies on social networks and the Internet, for example).

In both studies, aiming to allow the users to understand the visualization techniques and their possibilities of use in a real environment, they were invited to use the prototype in their day-to-day activities. To better understand and analyze this use, a qualitative approach [START_REF] Denzin | Collecting and Interpreting Qualitative Materials[END_REF], with a restricted number of users was chosen: first, the users were invited to use the prototype and, then, to answer a set of questions related to this use.

First Phase: General Public

The methodology used in the first phase and its results are presented as follows.

Used Methodology. In this phase, typical Twitter's users from the general public were invited to participate in the study. Following the example of the evaluations of the related work [START_REF] Barata | AppInsight: What have I been doing?[END_REF][START_REF] Leung | Exploring Social Networks: a frequent pattern visualization approach[END_REF][START_REF] Marcus | TwitInfo: aggregating and visualizing microblogs for event exploration[END_REF], which were performed respectively with 10, 12 and 12 users, we invited 11 users to use DeepTwitter and then answer an online questionnaire about that use. The online questionnaire contained 21 open and closed questions about the user profile and his experience with Twitter and DeepTwitter.

By agreeing to participate, the users received a copy of the prototype and were free to use any visualization technique available from the tool for about a week and at any time they so wished. It is worthwhile mentioning that this use was made in an individual way and it was not accompanied by any observer. After this use they were instructed to answer the online questionnaire. The questionnaire contained open and closed questions, about the user profile, the Twitter and the prototype.

Results. Most users that answered the questionnaire were students (82%) and from the Social Communication area (55% of the students).

Regarding the use of Twitter, we found that most users (91%) had used it for two years or more and 64% used it every day. When questioned about why they used this social network, the main choices were (the users could choose more than one option): personal purposes (64%), work (36%) and information (36%).

Regarding the use of DeepTwitter, 64% used it for one week, mostly using the prototype less than once a day (64%). In relation to the visualization tools available in it, the Social Networking Visualization was the most popular tool, chosen by 64% of the users when asked about the main advantage of using DeepTwitter and by 73% when asked about the visualization tool they considered the most interesting. The possibility to classify the tweets was also pointed out by 27% of users.

The preference for these two visualization toolssocial networking and tweets classificationwas also highlighted in the users comments: "This visualization facilitates the verification of users who I follow that also follow me, which is very interesting", "The social networking is cool because you can check nearby connection; the tweets per group also facilitate some 'conversations'", and "tweets classification per tag is a good function for organizing content".

Beyond the scope of personal use, one possible application was highlighted by two distinct users: the use by companies. One of the users pointed out that for a company this Social Networking visualization tool would be very useful for the connection with potential new followers: "Especially for a business twitter, this tool helps you connect to new followers that have to do with the company subject. For example, through the social networking visualization I can see who are the followers in common among my followers, it helps me follow relevant people in my twitter". Another user proposes the use of tags classification for this purpose: "for a company, for example, launching a promotional tag".

Second Phase: Professionals

The methodology used in the second phase and its results are presented as follows.

Used Methodology. In line with the results of the first phase that pointed to a professional use of this kind of tool, we developed a second phase with professionals who use Twitter as a working tool, inviting them to freely use the prototype in their day-today work, and after this use, participate in an interview to report their impressions.

As occurred in the previous phase, the participants received a copy of the prototype to use, by themselves and without the help of an observer, for about a week. The three groups of participants in this phase were professional users, as will be described in the next section. After using the prototype, they participated in a semi-structured interview, conducted by members of the research team. The interview began with 14 questions about the user profile and his experience with Twitter and DeepTwitter.

Results. The participants interviewed represent three distinct profiles: (1) employees of a private company of web hosting that were responsible for the company's public relations (group composed by 3 participants, that answered the questions in group and used the tool with the company account; they will be identified here as User 1); (2) the team responsible for the Twitter account of the Faculty of Social Communication of a large private university in the country (group composed by about 4-6 participants, students of the University, who also responded in group and used the institutional account, so they will be identified here as User 2); and (3) a University professor of Journalism (User 3), who used the program with his Twitter account. The academic profiles of users covered the areas of Public Relations, Journalism and Administration, and their professional profiles are about Social Communication and Customer Relations.

Focusing specifically on the last part of the interview, the users connected to the area of relationships (Users 1 and 2), when asked about the main feature of the prototype, highlighted the Visualization of their followers. User 1 highlights that with the timelines they can "identify over time followers of our activities in order to be aligned with our audience". User 3, working with Journalism, believes that the main feature is "the ability to follow the conversations of specific groups of people". These points were confirmed when all users of this phase highlighted that the visualization tool they would use more often would be the timeline analysis. User 3 said: "For journalism or marketing, for example, the analysis of the timeline is a very useful feature".

Discussion

Some of the issues presented in the results will be discussed in accordance with the visualization techniques available.

Social Network Visualization.

From our studies, we found that for users of the general public the Social Network Visualization was considered the most attractive fea-ture. On the other hand, users from companies (professionals), who often focus on their followers' behavior, highlighted that this feature should be removed from the prototype because the visualization gets very confusing when showing a great number of followers. That is, to the general public this feature allows a very interesting view of their entire network of contacts (and the relationships between their followers and who they follow), while for users interested mainly in their followers' activity, with hundreds (and even thousands) of them, this way of viewing the network seemed useless, because the amount of users shown did not allow a clear view of these connections. In this case, alternative techniques for the visual analysis of large graphs [START_REF] Landesberger | Visual Analysis of Large Graphs: State-of-the-Art and Future Research Challenges[END_REF] could be presented, but that was out of the scope of this research.

Timeline and Tweets Classification. To users concerned with the relationship area, the timelines allow them to monitor the activities of their followers and thus find a better way to interact with them, knowing the best time of the day to send them an important notification or greeting message, for instance. From a journalistic point of view, the possibility of viewing the timelines from user groups or the classification of tweets permits focusing on specific issues (or conversations), enabling the analysis of new perspectives (how a particular post was discussed, how a particular group reacted to certain news, and so on), thus generating new information ("news").

Tag Cloud and Most Popular Users. Since Tag Cloud and Most Popular Users are visualizations techniques very common nowadays, these were the least mentioned in the evaluation. Only one user highlighted the possibility of using the tag cloud for mapping the subjects most in vogue at the moment. Another one mentioned that the most popular users could be used in order to evaluate the influence of their followers.

Conclusion and Future Works

The increase in use of social networks has been intense in recent years, as intense as the activity of users on these networks. Understanding the behavior of users on these networks, e.g. to companies' public relations identify a better way to get in touch with them, can be a hard task, given their high level of interactivity. In this context, we discussed how information visualization techniques could be helpful in supporting this task, through a set of standard visualization techniques and the discussion of these visualizations with its potential users.

From the analyses carried out we can clearly see that different profiles of users are interested in different features (and in different visualization ways), as noted with the preference of users in general (general public) for the social network visualization, and professional users for the timelines. In addition, a comment from one of the users (professional profile) that believes that everything the Twitter already offers should be excluded from the prototype led us to reflect.

Adding to this reflection the trend of users to simultaneously use several small applications to monitor the use of social networks, we now are designing distinct small visualization applications, focusing on specific market niches. Our first application is aimed specifically at professionals working in the area of relationships and their needs, exploring features as visualization of feelings and statistics about mentions.

Focusing on the information visualization area, the interest of users in the visual exploration of the available information opens the opportunity to research and develop novel interactive visualization techniques, focused specifically on social networks, and, also, in multi touch devices that are widely used nowadays and could provide new interactive approaches to visualization.

Fig. 1 .

 1 Fig. 1. The user social network with two groups of users and a window containing information about the selected user.

Fig. 2 .

 2 Fig. 2. Example of a Timeline with tweets classification.

Acknowledgments. This work was partially supported by PUCRS (Edital 07/2012 -Programa de Apoio a Integração Entre Áreas/PRAIAS).