to_be_classified: A Facet Analysis of a Folksonomy

This research examines Ranganathan’s postulational approach to facet analysis with the intention of manually inducing a faceted classification ontology from a folksonomy. Folksonomies are viewed as a source to a wealth of data representing users’ perspectives. An in-depth study of faceted classification theory is used to form a methodology based on the postulational approach. The dataset used to test the methodology consists of over 107,000 instances of 1,275 unique tags representing 76 popular non-fiction history books collected from the LibraryThing folksonomy. Preliminary results of the facet analysis indicate the manual inducement of two faceted classification ontologies in the dataset; one representing the universe of books and one representing the universe of subjects within the universe of books. The ontology representing the universe of books is considered to be complete, whereas the ontology representing the universe of subjects is incomplete. These differences are discussed in light of theoretical differences between special and universal faceted classifications. The induced ontologies are then discussed in terms of their substantiation or violation of Ranganathan’s Canons of Classification.Master i bibliotek- og informasjonsvitenska

Computing point-of-view : modeling and simulating judgments of taste

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 153-163).People have rich points-of-view that afford them the ability to judge the aesthetics of people, things, and everyday happenstance; yet viewpoint has an ineffable quality that is hard to articulate in words, let alone capture in computer models. Inspired by cultural theories of taste and identity, this thesis explores end-to-end computational modeling of people's tastes-from model acquisition, to generalization, to application- under various realms. Five aesthetical realms are considered-cultural taste, attitudes, ways of perceiving, taste for food, and sense-of-humor. A person's model is acquired by reading her personal texts, such as a weblog diary, a social network profile, or emails. To generalize a person model, methods such as spreading activation, analogy, and imprimer supplementation are applied to semantic resources and search spaces mined from cultural corpora. Once a generalized model is achieved, a person's tastes are brought to life through perspective-based applications, which afford the exploration of someone else's perspective through interactivity and play. The thesis describes model acquisition systems implemented for each of the five aesthetical realms.(cont.) The techniques of 'reading for affective themes' (RATE), and 'culture mining' are described, along with their enabling technologies, which are commonsense reasoning and textual affect analysis. Finally, six perspective-based applications were implemented to illuminate a range of real-world beneficiaries to person modeling-virtual mentoring, self-reflection, and deep customization.by Xinyu Hugo Liu.Ph.D

Serious leisure in the digital world: exploring the information behaviour of fan communities

This research investigates the information behaviour of cult media fan communities on the internet, using three novel methods which have not previously been applied to this domain. Firstly, a review, analysis and synthesis of the literature related to fan information behaviour, both within the disciplines of LIS and fan studies, revealed unique aspects of fan information behaviour, particularly in regards to produsage, copyright, and creativity. The findings from this literature analysis were subsequently investigated further using the Delphi method and tag analysis. A new Delphi variant – the Serious Leisure Delphi – was developed through this research. The Delphi study found that participants expressed the greatest levels of consensus on statements on fan behaviour that were related to information behaviour and information-related issues. Tag analysis was used in a novel way, as a tool to examine information behaviour. This found that fans have developed a highly granular classification system for fanworks, and that on one particular repository a ‘curated folksonomy’ was being used with great success. Fans also use tags for a variety of reasons, including communicating with one another, and writing meta-commentary on their posts. The research found that fans have unique information behaviours related to classification, copyright, entrepreneurship, produsage, mentorship and publishing. In the words of Delphi participants – “being in fandom means being in a knowledge space,” and “fandom is a huge information hub just by existing”. From these findings a model of fan information behaviour has been developed, which could be further tested in future research

Serious Leisure In The Digital World: Exploring The Information Behaviour Of Fan Communities

This research investigates the information behaviour of cult media fan communities on the internet, using three novel methods which have not previously been applied to this domain. Firstly, a review, analysis and synthesis of the literature related to fan information behaviour, both within the disciplines of LIS and fan studies, revealed unique aspects of fan information behaviour, particularly in regards to produsage, copyright, and creativity. The findings from this literature analysis were subsequently investigated further using the Delphi method and tag analysis. A new Delphi variant – the Serious Leisure Delphi – was developed through this research. The Delphi study found that participants expressed the greatest levels of consensus on statements on fan behaviour that were related to information behaviour and information-related issues. Tag analysis was used in a novel way, as a tool to examine information behaviour. This found that fans have developed a highly granular classification system for fanworks, and that on one particular repository a ‘curated folksonomy’ was being used with great success. Fans also use tags for a variety of reasons, including communicating with one another, and writing meta-commentary on their posts. The research found that fans have unique information behaviours related to classification, copyright, entrepreneurship, produsage, mentorship and publishing. In the words of Delphi participants – “being in fandom means being in a knowledge space,” and “fandom is a huge information hub just by existing”. From these findings a model of fan information behaviour has been developed, which could be further tested in future research

Knowing together

In den letzten Jahren sind eine Reihe neuer Anwendungen im Internet entstanden, die zumeist als Web2.0 oder social software bezeichnet werden. Viele dieser Anwendungen sind gekennzeichnet durch die Einbindung mehrerer Agenten in Prozesse zur Verbreitung, Organisation und Herstellung von Wissen. Das Ziel der vorliegenden Dissertation besteht in der Analyse der epistemologischen Relevanz dieser epistemischen social software Anwendungen. Da die Kommunikation und Interaktion zwischen mehreren Agenten deren Schlüsselmerkmal darstellt, bildet die Soziale Erkenntnistheorie als philosophische Disziplin, welche die Weisen untersucht, in denen Wissen sozial bedingt ist, die theoretische Grundlage für die Analyse der epistemischen Prozesse innerhalb dieser Systeme. Weil bisher keine soziale Erkenntnistheorie eine ausreichende Theorie für die Analyse epistemischer social software zur Verfügung stellen konnte, habe ich die Grundlagen für ein neues sozio-epistemisches Model entwickelt, welches zwar im sozio-epistemologischen Diskurs verankert ist, jedoch um Erkenntnisse aus dem Feld der Science and Technology Studies (STS) erweitert wurde. Dieses Model gründet in der Klassifikation von sozio-technischen epistemischen Systemen anhand unterschiedlicher Mechanismen der Schließung, welche zur Beendigung sozio-epistemischer Prozesse verwendet werden. Diese Klassifikation anhand der drei Schließungsmechanismen Integration, Aggregation und Selektion zielt nicht auf die Einebnung der Differenzen zwischen sozio-technischen epistemischen Systemen, vielmehr liegt ihr Wert in ihrer heuristischen Fruchtbarkeit, darin Differenzen aufzumachen. Systeme, welche unterschiedliche Schließungsmechanismen nutzen, sind gebunden an unterschiedliche soziale, technische und epistemische Voraussetzungen, sie haben unterschiedliche Stärken und Schwächen und eignen sich daher für unterschiedliche epistemische Aufgaben. Das von mir entwickelte Modell lenkt dabei die Aufmerksamkeit auf ein bislang weitgehend in der sozialen Erkenntnistheorie vernachlässigtes Thema: das Technische und seine Beziehung zum Sozialen und zum Epistemischen. Da die meisten epistemischen Praktiken heute durchdrungen sind von Technologie, ist deren Berücksichtigung von entscheidender Bedeutung für jede soziale Erkenntnistheorie, die beansprucht, nicht nur normativ angemessen, sondern auch empirisch adäquat zu sein.In recent years new applications emerged on the Web which received the labels Web2.0 or social software. In many of these applications people are engaged in epistemic activities, such as the dissemination, organization or creation of knowledge. The goal of this thesis is to analyze the epistemological relevance of such epistemic social software. Because communication and interaction between multiple agents seems to be the key to understand the epistemic processes within such systems, social epistemology, the philosophical discipline exploring the ways and the extent to which knowledge is social, was chosen as a theoretical framework. However, none of the existing comprehensive social epistemologies delivers a sufficient framework to analyze epistemic social software. Therefore, I have developed a new socio-epistemological framework to analyze epistemic social software which is rooted in socio-epistemological discourse, but amends it with insights from the field of Science and Technology Studies (STS). My framework is founded on a tripartite classification of socio-technical epistemic system based on the mechanisms they employ to close socio-epistemic processes. These three mechanisms are integration, aggregation and selection. With this classification I do not aim at reducing the differences between systems to their mechanisms of closure. However, I argue that the classification based on this indicator is heuristically fruitful. Systems employing different mechanisms of closure depend on different social, technical and epistemic prerequisites, have different strengths and weaknesses and are optimal for different epistemic tasks. My model puts a fact into the focus that has been neglected so far in social epistemology: the technical and its relationship to the social and the epistemic. Since most epistemic practices are nowadays pervaded by technologies, such a consideration of the role of technologies in these practices seems to be indispensable for any social epistemology that aims at being not only normatively appropriate, but also empirically adequate

Social impact retrieval: measuring author influence on information retrieval

The increased presence of technologies collectively referred to as Web 2.0 mean the entire process of new media production and dissemination has moved away from an authorcentric approach. Casual web users and browsers are increasingly able to play a more active role in the information creation process. This means that the traditional ways in which information sources may be validated and scored must adapt accordingly. In this thesis we propose a new way in which to look at a user's contributions to the network in which they are present, using these interactions to provide a measure of authority and centrality to the user. This measure is then used to attribute an query-independent interest score to each of the contributions the author makes, enabling us to provide other users with relevant information which has been of greatest interest to a community of like-minded users. This is done through the development of two algorithms; AuthorRank and MessageRank. We present two real-world user experiments which focussed around multimedia annotation and browsing systems that we built; these systems were novel in themselves, bringing together video and text browsing, as well as free-text annotation. Using these systems as examples of real-world applications for our approaches, we then look at a larger-scale experiment based on the author and citation networks of a ten year period of the ACM SIGIR conference on information retrieval between 1997-2007. We use the citation context of SIGIR publications as a proxy for annotations, constructing large social networks between authors. Against these networks we show the effectiveness of incorporating user generated content, or annotations, to improve information retrieval

Personalized information retrieval based on time-sensitive user profile

Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet objectif, au lieu de prendre en considération l'appariement requête-document uniquement, les SRI s'intéressent aussi au contexte de l'utilisateur. En effet, le profil utilisateur a été considéré dans la littérature comme l'élément contextuel le plus important permettant d'améliorer la pertinence de la recherche. Il est intégré dans le processus de recherche d'information afin d'améliorer l'expérience utilisateur en recherchant des informations spécifiques. Comme le facteur temps a gagné beaucoup d'importance ces dernières années, la dynamique temporelle est introduite pour étudier l'évolution du profil utilisateur qui consiste principalement à saisir les changements du comportement, des intérêts et des préférences de l'utilisateur en fonction du temps et à actualiser le profil en conséquence. Les travaux antérieurs ont distingué deux types de profils utilisateurs : les profils à court-terme et ceux à long-terme. Le premier type de profil est limité aux intérêts liés aux activités actuelles de l'utilisateur tandis que le second représente les intérêts persistants de l'utilisateur extraits de ses activités antérieures tout en excluant les intérêts récents. Toutefois, pour les utilisateurs qui ne sont pas très actifs dont les activités sont peu nombreuses et séparées dans le temps, le profil à court-terme peut éliminer des résultats pertinents qui sont davantage liés à leurs intérêts personnels. Pour les utilisateurs qui sont très actifs, l'agrégation des activités récentes sans ignorer les intérêts anciens serait très intéressante parce que ce type de profil est généralement en évolution au fil du temps. Contrairement à ces approches, nous proposons, dans cette thèse, un profil utilisateur générique et sensible au temps qui est implicitement construit comme un vecteur de termes pondérés afin de trouver un compromis en unifiant les intérêts récents et anciens. Les informations du profil utilisateur peuvent être extraites à partir de sources multiples. Parmi les méthodes les plus prometteuses, nous proposons d'utiliser, d'une part, l'historique de recherche, et d'autre part les médias sociaux. En effet, les données de l'historique de recherche peuvent être extraites implicitement sans aucun effort de l'utilisateur et comprennent les requêtes émises, les résultats correspondants, les requêtes reformulées et les données de clics qui ont un potentiel de retour de pertinence/rétroaction. Par ailleurs, la popularité des médias sociaux permet d'en faire une source inestimable de données utilisées par les utilisateurs pour exprimer, partager et marquer comme favori le contenu qui les intéresse. En premier lieu, nous avons modélisé le profil utilisateur utilisateur non seulement en fonction du contenu de ses activités mais aussi de leur fraîcheur en supposant que les termes utilisés récemment dans les activités de l'utilisateur contiennent de nouveaux intérêts, préférences et pensées et doivent être pris en considération plus que les anciens intérêts surtout que de nombreux travaux antérieurs ont prouvé que l'intérêt de l'utilisateur diminue avec le temps. Nous avons modélisé le profil utilisateur sensible au temps en fonction d'un ensemble de données collectées de Twitter (un réseau social et un service de microblogging) et nous l'avons intégré dans le processus de reclassement afin de personnaliser les résultats standards en fonction des intérêts de l'utilisateur.En second lieu, nous avons étudié la dynamique temporelle dans le cadre de la session de recherche où les requêtes récentes soumises par l'utilisateur contiennent des informations supplémentaires permettant de mieux expliquer l'intention de l'utilisateur et prouvant qu'il n'a pas trouvé les informations recherchées à partir des requêtes précédentes.Ainsi, nous avons considéré les interactions récentes et récurrentes au sein d'une session de recherche en donnant plus d'importance aux termes apparus dans les requêtes récentes et leurs résultats cliqués. Nos expérimentations sont basés sur la tâche Session TREC 2013 et la collection ClueWeb12 qui ont montré l'efficacité de notre approche par rapport à celles de l'état de l'art. Au terme de ces différentes contributions et expérimentations, nous prouvons que notre modèle générique de profil utilisateur sensible au temps assure une meilleure performance de personnalisation et aide à analyser le comportement des utilisateurs dans les contextes de session de recherche et de médias sociaux.Recently, search engines have become the main source of information for many users and have been widely used in different fields. However, Information Retrieval Systems (IRS) face new challenges due to the growth and diversity of available data. An IRS analyses the query submitted by the user and explores collections of data with unstructured or semi-structured nature (e.g. text, image, video, Web page etc.) in order to deliver items that best match his/her intent and interests. In order to achieve this goal, we have moved from considering the query-document matching to consider the user context. In fact, the user profile has been considered, in the literature, as the most important contextual element which can improve the accuracy of the search. It is integrated in the process of information retrieval in order to improve the user experience while searching for specific information. As time factor has gained increasing importance in recent years, the temporal dynamics are introduced to study the user profile evolution that consists mainly in capturing the changes of the user behavior, interests and preferences, and updating the profile accordingly. Prior work used to discern short-term and long-term profiles. The first profile type is limited to interests related to the user's current activities while the second one represents user's persisting interests extracted from his prior activities excluding the current ones. However, for users who are not very active, the short-term profile can eliminate relevant results which are more related to their personal interests. This is because their activities are few and separated over time. For users who are very active, the aggregation of recent activities without ignoring the old interests would be very interesting because this kind of profile is usually changing over time. Unlike those approaches, we propose, in this thesis, a generic time-sensitive user profile that is implicitly constructed as a vector of weighted terms in order to find a trade-off by unifying both current and recurrent interests. User profile information can be extracted from multiple sources. Among the most promising ones, we propose to use, on the one hand, searching history. Data from searching history can be extracted implicitly without any effort from the user and includes issued queries, their corresponding results, reformulated queries and click-through data that has relevance feedback potential. On the other hand, the popularity of Social Media makes it as an invaluable source of data used by users to express, share and mark as favorite the content that interests them. First, we modeled a user profile not only according to the content of his activities but also to their freshness under the assumption that terms used recently in the user's activities contain new interests, preferences and thoughts and should be considered more than old interests. In fact, many prior works have proved that the user interest is decreasing as time goes by. In order to evaluate the time-sensitive user profile, we used a set of data collected from Twitter, i.e a social networking and microblogging service. Then, we apply our re-ranking process to a Web search system in order to adapt the user's online interests to the original retrieved results. Second, we studied the temporal dynamics within session search where recent submitted queries contain additional information explaining better the user intent and prove that the user hasn't found the information sought from previous submitted ones. We integrated current and recurrent interactions within a unique session model giving more importance to terms appeared in recently submitted queries and clicked results. We conducted experiments using the 2013 TREC Session track and the ClueWeb12 collection that showed the effectiveness of our approach compared to state-of-the-art ones. Overall, in those different contributions and experiments, we prove that our time-sensitive user profile insures better performance of personalization and helps to analyze user behavior in both session search and social media contexts

Critical point of view: a Wikipedia reader

For millions of internet users around the globe, the search for new knowledge begins with Wikipedia. The encyclopedia’s rapid rise, novel organization, and freely offered content have been marveled at and denounced by a host of commentators. Critical Point of View moves beyond unflagging praise, well-worn facts, and questions about its reliability and accuracy, to unveil the complex, messy, and controversial realities of a distributed knowledge platform. The essays, interviews and artworks brought together in this reader form part of the overarching Critical Point of View research initiative, which began with a conference in Bangalore (January 2010), followed by events in Amsterdam (March 2010) and Leipzig (September 2010). With an emphasis on theoretical reflection, cultural difference and indeed, critique, contributions to this collection ask: What values are embedded in Wikipedia’s software? On what basis are Wikipedia’s claims to neutrality made? How can Wikipedia give voice to those outside the Western tradition of Enlightenment, or even its own administrative hierarchies? Critical Point of View collects original insights on the next generation of wiki-related research, from radical artistic interventions and the significant role of bots to hidden trajectories of encyclopedic knowledge and the politics of agency and exclusion

JPEG: the quadruple object

The thesis, together with its practice-research works, presents an object-oriented perspective on the JPEG standard. Using the object-oriented philosophy of Graham Harman as a theoretical and also practical starting point, the thesis looks to provide an account of the JPEG digital object and its enfolding within the governmental scopic regime. The thesis looks to move beyond accounts of digital objects and protocols within software studies that position the object in terms of issues of relationality, processuality and potentiality. From an object-oriented point of view, the digital object must be seen as exceeding its relations, as actual, present and holding nothing in reserve. The thesis presents an account of JPEG starting from that position as well as an object-oriented account of JPEG’s position within the distributed, governmental scopic regime via an analysis of Facebook’s Timeline, tagging and Haystack systems. As part of a practice-research project, the author looked to use that perspective within photographic and broader imaging practices as a spur to new work and also as a “laboratory” to explore Harman’s framework. The thesis presents the findings of those “experiments” in the form of a report alongside practice-research eBooks. These works were not designed to be illustrations of the theory, nor works to be “analysed”. Rather, following the lead of Ian Bogost and Mark Amerika, they were designed to be “philosophical works” in the sense of works that “did” philosophy