BibSonomyburst/tag/SocialBibSonomy RSS feed for /tag/Social2012-02-15T06:44:16+01:00http://www.bibsonomy.org/bibtex/27e19eb58ea29652cf8f94b9c44ec6ede/cdifsinarbonnecdifsinarbonne2012-02-14T12:22:04+01:00Afrique Belgique Canada France Maghreb Suisse accompagnement acculturation accès-aux-soins altérité annonce-de-la-maladie anthropocentrisme anthropologie-de-la-santé anthropologie-médicale bibliographie bien-être communication communication-interculturelle concept confiance consultation-transculturelle contre-transfert cosmocentrisme croyance croyances culture déficience-intellectuelle ethnomédecine ethnopsychiatrie identité-culturelle information interculturalité interprétariat logopédie maladie migrants mort multiculturalité médecine-alternative médiation-interculturelle oncologie peur promotion-de-la-santé préjugés prévention psychiatrie psychothérapie qualité-des-soins relation-soignant-soigné relation-thérapeutique représentations représentations-sociales santé santé-mentale santé-publique social soins-infirmiers soins-à-domicile souffrance temporalité traditions transculturation transfert travail valeurs éthique <span class="authorEditorList"><a href="/author/Collectif"> Collectif</a> </span>(<em>September 2011</em>)<em>31 p. Doc électronique - Ressources documentaires secondaires . </em>Tue Feb 14 12:22:04 CET 2012septembre31 p. Doc électronique - Ressources documentaires secondairesRelation soignant-soigné dans un contexte multiculturel2011Afrique Belgique Canada France Maghreb Suisse accompagnement acculturation accès-aux-soins altérité annonce-de-la-maladie anthropocentrisme anthropologie-de-la-santé anthropologie-médicale bibliographie bien-être communication communication-interculturelle concept confiance consultation-transculturelle contre-transfert cosmocentrisme croyance croyances culture déficience-intellectuelle ethnomédecine ethnopsychiatrie identité-culturelle information interculturalité interprétariat logopédie maladie migrants mort multiculturalité médecine-alternative médiation-interculturelle oncologie peur promotion-de-la-santé préjugés prévention psychiatrie psychothérapie qualité-des-soins relation-soignant-soigné relation-thérapeutique représentations représentations-sociales santé santé-mentale santé-publique social soins-infirmiers soins-à-domicile souffrance temporalité traditions transculturation transfert travail valeurs éthique Ce dossier thématique présente des ressources documentaires liées aux relations qu’entretiennent les professionnels de la santé et leurs patients d’origines culturelles diverses. Il s’organise sous la forme d’une sélection bibliographique regroupant les publications liées : à la relation soignant-soigné dans son acception la plus large, puis de manière plus précise à l’approche interculturelle dans les soins, aux diverses représentations de la maladie et de la santé et aux apports de l’anthropologie médicale et de l’anthropologie de la santé, à la communication et à la médiation interculturelle, et plus spécifiquement au recours à des interprètes ou médiateurs, aux sujets plus spécifiques comme la relation soignant-soigné dans le domaine de la santé mentale ainsi que l’impact de certaines croyances et religions, aux outils pédagogiques et aux sites sur le sujet.http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Tue Feb 14 08:29:50 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/jaeschkejaeschke2012-02-13T12:52:23+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Mon Feb 13 12:52:23 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/295d970d410501662dc97e4ca8fbd9afa/kremplokremplo2012-02-12T22:54:26+01:00anatomy clusters communication investors real social socialnetworkanalysis trading visualizing <span class="authorEditorList"><a href="/author/Tumminello">Michele Tumminello</a>, <a href="/author/Lillo">Fabrizio Lillo</a>, <a href="/author/Piilo">Jyrki Piilo</a>, and <a href="/author/Mantegna">Rosario N Mantegna</a> </span><em>New Journal of Physics</em> <em>14(1):013041</em> (<em>2012</em>)Sun Feb 12 22:54:26 CET 2012New Journal of Physics1013041Identification of clusters of investors from their real trading activity in a financial market142012anatomy clusters communication investors real social socialnetworkanalysis trading visualizing We use statistically validated networks, a recently introduced method of validating links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing us to track the trading activity of individual investors of Nokia stock. We find that many statistically detected clusters of investors show a very high degree of synchronization in time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and find that several of them show an over-expression of specific categories of investors.http://www.bibsonomy.org/bibtex/2cde4afb2071897cea35fef6991a3ccdb/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Hunter">Jane Hunter</a> </span><em>Annual Review of Information Science and Technology</em> <em>43(1):1--84</em> (<em>2009</em>)Sat Feb 11 19:24:16 CET 2012Annual Review of Information Science and Technology11--84Collaborative semantic tagging and annotation systems432009Social Tagging http://www.bibsonomy.org/bibtex/21dbd619dec493e0b1d117b7f0649c905/peter.b825peter.b8252012-02-11T19:24:16+01:00Definition Social Tagging;Expose <span class="authorEditorList"><a href="/author/Golder">S. A. Golder</a>, and <a href="/author/Huberman">B. A. Huberman</a> </span><em>Journal of Information Science</em> <em>32(2):198--208</em> (<em>2006</em>)Sat Feb 11 19:24:16 CET 2012Journal of Information Science2198--208Usage patterns of collaborative tagging systems322006Definition Social Tagging;Expose http://www.bibsonomy.org/bibtex/2e90f53e32cd8a0060eada71fd71ca668/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Liao">Chechen Liao</a>, <a href="/author/To">Pui-Lai To</a>, <a href="/author/Liu">Chuang-Chun Liu</a>, <a href="/author/Kuo">Pu-Yuan Kuo</a>, and <a href="/author/Chuang">Shu-Hui Chuang</a> </span><em>Online Information Review</em> <em>35(2):237--254</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Online Information Review2237--254Factors influencing the intended use of web portals352011Social Tagging Purpose -- A lack of differentiation in the function and appearance of web portals has led to fierce competition; attracting users' attention is no doubt the most important factor for portal success. This study aims to combine rational assessment factors and non-rational assessment factors to examine their impact on the intended use of portals.Design/methodology/approach -- The research participants were individuals who have experience using web portals. A total of 215 valid questionnaires were collected. Structural equation modelling was used to test the research hypothesis.Findings -- This study is based on the technology acceptance model (TAM), a significant model in MIS research. The results show that all assessment factors have an impact on the intended use of web portals.Research limitations/implications -- TAM emphasised perceived usefulness as the key determinant of user acceptance of technology. This study finds, however, that in the portal context, non-rational assessment factors such as perceived playfulness and habit have more significant effects on users' intention than perceived usefulness.Practical implications -- The paper concludes that non-rational assessment factors are important boundary conditions to the validity of the technology acceptance model. To attract users, web site designers should be more aware of aesthetics and the hedonic nature of web users.Originality/value -- This study has contributed to the original TAM by incorporating non-rational assessment factors. It also validates this empirical model. The results of this study can help practitioners create a more successful business model and help researchers better understand user behaviour on the internet.http://www.bibsonomy.org/bibtex/2587429e59eb2e978dd4cfb6178913d93/peter.b825peter.b8252012-02-11T19:24:16+01:00Comparison: Social Tags/Keywords <span class="authorEditorList"><a href="/author/Kipp">Margaret E. I. Kipp</a> </span><em>Canadian Journal of Information \&amp; Library Sciences</em> <em>29(4):419--436</em> (<em>2005</em>)Sat Feb 11 19:24:16 CET 2012Canadian Journal of Information {\&} Library Sciences4419--436Complementary or Discrete Contexts in Online Indexing: A Comparison of User, Creator and Intermediary Keywords292005Comparison: Social Tags/Keywords This paper (forthcoming in the Canadian Journal of Information and Library Science) and presented at the CAIS Conference examines the context of online indexing from the viewpoint of three different groups: users, authors, and intermediaries. User, author and intermediary keywords were collected from journal articles tagged on citeulike and analysed. Descriptive statistics and thesaural term comparison shows that there are important differences in the context of keywords from the three groups.http://www.bibsonomy.org/bibtex/2593b22c8c6c0452ad8868b388d840251/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Barragans Martinez">Ana Barragans Martinez</a>, <a href="/author/Rey Lopez">Marta Rey Lopez</a>, <a href="/author/Costa Montenegro">Enrique Costa Montenegro</a>, <a href="/author/Mikic Fonte">Fernando Mikic Fonte</a>, <a href="/author/Burguillo">Juan Burguillo</a>, and <a href="/author/Peleteiro">Ana Peleteiro</a> </span><em>IEEE Internet Computing</em> <em>14(6):23--30</em> (<em>2010</em>)Sat Feb 11 19:24:16 CET 2012IEEE Internet Computing623--30Exploiting Social Tagging in a Web 2.0 Recommender System142010Social Tagging Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take advantage of Web 2.0 applications, the authors propose using information obtained from social tagging to improve the recommendations. The Web 2.0 TV program recommender queveo.tv currently combines content-based and collaborative filtering techniques. This article presents a novel tag-based recommender to enhance the recommending engine by improving the coverage and diversity of the suggestions.http://www.bibsonomy.org/bibtex/25b7944ce1b5d1967113328a636ecf502/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Frohner">Herbert Frohner</a> </span><em>Web 2.0 </em><em>Hülsbusch, </em><em>Boizenburg, </em>(<em>2010</em>)Sat Feb 11 19:24:16 CET 2012BoizenburgWeb 2.0Social Tagging: Grundlagen, Anwendungen, Auswirkungen auf Wissensorganisation und soziale Strukturen der User2010Social Tagging Social Tagging ist eine Methode zur semantischen Datenorganisation. Im Unterschied zu traditionellen Ans{\"a}tzen wird die Kategorisierung nicht von Experten vorgenommen, sondern von einer Vielzahl von Benutzern gemeinschaftlich entwickelt. Bez{\"u}glich der Daten existieren grunds{\"a}tzlich keinerlei Einschr{\"a}nkungen. Dabei kann es sich sowohl um multimediale Inhalte als auch um wissenschaftliche Literatur handeln. Jeder Benutzer, unabh{\"a}ngig von Expertise oder Intention, ist aufgefordert, mithilfe von frei gew{\"a}hlten Tags die Kategorisierung der verwendeten Ressourcen zu unterst{\"u}tzen. Insgesamt entsteht dadurch eine Sammlung verschiedenster subjektiver Einsch{\"a}tzungen, die zusammen eine umfassende semantische Organisation bestimmter Inhalte darstellen.Ziel dieses Buches ist es, zun{\"a}chst die Grundlagen und Anwendungen von Social Tagging zu er{\"o}rtern und dann speziell die Effekte im Hinblick auf die Wissensorganisation und die sozialen Beziehungen der Benutzer zu analysieren.Eines der zentralen Ergebnisse dieser Arbeit ist die Erkenntnis, dass die gemeinschaftlich erzeugten Metadaten eine unerwartet hohe Qualit{\"a}t bzw. Bedeutsamkeit aufweisen, obwohl Mehrdeutigkeiten und verschiedene Schreibweisen diese negativ beeinflussen k{\"o}nnten. Social Tagging ist besonders effektiv f{\"u}r die Organisation von sehr gro{\ss}en oder auch heterogenen Daten-best{\"a}nden, die mit herk{\"o}mmlichen, experten-basierten Kategorisierungsverfahren nicht mehr verarbeitet werden k{\"o}nnen oder durch automa-tische Verfahren qualitativ schlechter indexiert werden. Durch Social Tagging wird nicht nur die Wissensorganisation gef{\"o}rdert, sondern dar{\"u}ber hinaus auch die Zusammenarbeit und der Aufbau von Communities, weshalb Social Tagging auch effizient in der Lehre eingesetzt werden kann.http://www.bibsonomy.org/bibtex/28466b0961c1dc698c00e855cbd47a2c1/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Chua">Alton Y. K. Chua</a>, <a href="/author/Razikin">Khasfariyati Razikin</a>, and <a href="/author/Goh">Dion H. Goh</a> </span><em>Journal of Information Science</em> <em>37(1):3--18</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Journal of Information Science13--18Social tags as news event detectors372011Social Tagging http://www.bibsonomy.org/bibtex/23c487a0e2ac7263db78b592fc6689fd4/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Chen">Lin-Chih Chen</a> </span><em>Online Information Review</em> <em>35(1):9--33</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Online Information Review19--33Term suggestion with similarity measure based on semantic analysis techniques in query logs352011Social Tagging Purpose -- Term suggestion is a very useful information retrieval technique that tries to suggest relevant terms for users' queries, to help advertisers find more appropriate terms relevant to their target market. This paper aims to focus on the problem of using several semantic analysis methods to implement a term suggestion system.Design/methodology/approach -- Three semantic analysis techniques are adopted -- latent semantic indexing (LSI), probabilistic latent semantic indexing (PLSI), and a keyword relationship graph (KRG) -- to implement a term suggestion system.Findings -- This paper shows that using multiple semantic analysis techniques can give significant performance improvements.Research limitations/implications -- The suggested terms returned from the system may be out of date, since the system uses a batch processing mode to update the training parameter.Originality/value -- The paper shows that the benefit of the techniques is to overcome the problems of synonymy and polysemy over the information retrieval field, by using a vector space model. Moreover, an intelligent stopping strategy is proposed to save the required number of iterations for probabilistic latent semantic indexing.http://www.bibsonomy.org/bibtex/29319de8a9c3079946b416a23e44efc19/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging;Tag categorization <span class="authorEditorList"><a href="/author/Cantador">Iván Cantador</a>, <a href="/author/Konstas">Ioannis Konstas</a>, and <a href="/author/Jose">Joemon M. Jose</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>9(1):1--15</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Web Semantics: Science, Services and Agents on the World Wide Web11--15Categorising social tags to improve folksonomy-based recommendations92011Social Tagging;Tag categorization In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks.Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes.We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. The obtained concepts are then transformed into semantic classes that can be uniquely assigned to content- and context-based categories. The identification of subjective and organisational tags is based on natural language processing heuristics.We collected a representative dataset from Flickr social tagging system, and conducted an empirical study to categorise real tagging data, and evaluate whether the resultant tags categories really benefit a recommendation model using the Random Walk with Restarts method. The results show that content- and context-based tags are considered superior to subjective and organisational tags, achieving equivalent performance to using the whole tag space.http://www.bibsonomy.org/bibtex/22e0e25efc9663cff68ab8008fe0ea9a6/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging;Expose;Comparison: Tags/Keywords <span class="authorEditorList"><a href="/author/Lu">Caimei Lu</a>, <a href="/author/Park">Jung-ran Park</a>, and <a href="/author/Hu">Xiaohua Hu</a> </span><em>Journal of Information Science</em> <em>36(6):763--779</em> (<em>2010</em>)Sat Feb 11 19:24:16 CET 2012Journal of Information Science6763--779User tags versus expert-assigned subject terms: A comparison of LibraryThing tags and Library of Congress Subject Headings362010Social Tagging;Expose;Comparison: Tags/Keywords Social tagging, as a recent approach for creating metadata, has caught the attention of library and information science researchers. Many researchers recommend incorporating social tagging into the library environment and combining folksonomies with formal classification. However, some researchers are concerned with the quality issues of social annotation because of its uncontrolled nature. In this study, we compare social tags created by users from the LibraryThing website with the subject terms assigned by experts according to the Library of Congress Subject Headings (LCSH). The purpose of this study is to examine the difference and connections between social tags and expert-assigned subject terms and further explore the feasibility and obstacles of implementing social tagging in library systems. The results of our study show that it is possible to use social tags to improve the accessibility of library collections. However, the existence of non-subject-related tags may impede the application of social tagging in traditional library cataloguing systems.http://www.bibsonomy.org/bibtex/22db7e476229343f08807b6782d304834/peter.b825peter.b8252012-02-11T19:24:16+01:00Comparison: Social Tags/Keywords;Expose <span class="authorEditorList"><a href="/author/Rolla">Peter J. Rolla</a> </span><em>Library Resources and Technical Services</em> <em>53(3):68--77</em> (<em>2009</em>)Sat Feb 11 19:24:16 CET 2012Library Resources and Technical Services368--77User tags versus subject headings: Can user-supplied data improve subject access to library collections?532009Comparison: Social Tags/Keywords;Expose Some members of the library community, including the Library of Congress Working Group on the Future of Bibliographic Control, have suggested that libraries should open up their catalogs to allow users to add descriptive tags to the bibliographic data in catalog records. The web site LibraryThing currently permits its members to add such user tags to its records for books and therefore provides a useful resource to contrast with library bibliographic records. A comparison between the LibraryThing tags for a group of books and the library-supplied subject headings for the same books shows that users and catalogers approach these descriptors very differently. Because of these differences, user tags can enhance subject access to library materials, but they cannot entirely replace controlled vocabularies such as the Library of Congress subject headings.http://www.bibsonomy.org/bibtex/2585d79e38190256023f7b67c7f61e14f/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Tsai">Li-Chen Tsai</a>, <a href="/author/Hwang">Sheue-Ling Hwang</a>, and <a href="/author/Tang">Kuo-Hao Tang</a> </span><em>Online Information Review</em> <em>35(2):272--290</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Online Information Review2272--290Analysis of keyword-based tagging behaviors of experts and novices352011Social Tagging http://www.bibsonomy.org/bibtex/2b73bf457ee6d8306c103a4ce8ae13ede/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Trant">Jennifer Trant</a> </span><em>Journal of Digital Information</em> (<em>2009</em>)Sat Feb 11 19:24:16 CET 2012Journal of Digital Information1Studying Social Tagging and Folksonomy: A Review and Framework102009Social Tagging http://www.bibsonomy.org/bibtex/2efb6cb6220dfdd1e3d9ca4894e9f1459/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Milicevic">Aleksandra Milicevic</a>, <a href="/author/Nanopoulos">Alexandros Nanopoulos</a>, and <a href="/author/Ivanovic">Mirjana Ivanovic</a> </span><em>Artificial Intelligence Review</em> <em>33(3):187--209</em> (<em>2010</em>)Sat Feb 11 19:24:16 CET 2012Artificial Intelligence Review3187--209Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions332010Social Tagging Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.http://www.bibsonomy.org/bibtex/292a4c4bf747c5f8342109f113b3123c3/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Lucassen">Teun Lucassen</a>, and <a href="/author/Schraagen">Jan Maarten Schraagen</a> </span><em>Journal of the American Society for Information Science and Technology</em> (<em>2011</em>)Sat Feb 11 19:24:16 CET 2012Journal of the American Society for Information Science and Technologyn/aFactual accuracy and trust in information: The role of expertise2011Social Tagging In the past few decades, the task of judging the credibility of information has shifted from trained professionals (e.g., editors) to end users of information (e.g., casual Internet users). Lacking training in this task, it is highly relevant to research the behavior of these end users. In this article, we propose a new model of trust in information, in which trust judgments are dependent on three user characteristics: source experience, domain expertise, and information skills. Applying any of these three characteristics leads to different features of the information being used in trust judgments; namely source, semantic, and surface features (hence, the name 3S-model). An online experiment was performed to validate the 3S-model. In this experiment, Wikipedia articles of varying accuracy (semantic feature) were presented to Internet users. Trust judgments of domain experts on these articles were largely influenced by accuracy whereas trust judgments of novices remained mostly unchanged. Moreover, despite the influence of accuracy, the percentage of trusting participants, both experts and novices, was high in all conditions. Along with the rationales provided for such trust judgments, the outcome of the experiment largely supports the 3S-model, which can serve as a framework for future research on trust in information.http://www.bibsonomy.org/bibtex/265f93041c7b3560e0d5457a38ad82350/peter.b825peter.b8252012-02-11T19:24:16+01:00Social Tagging <span class="authorEditorList"><a href="/author/Breslin">John G. Breslin</a>, <a href="/author/Passant">Alexandre Passant</a>, <a href="/author/Decker">Stefan Decker</a>, <a href="/author/Breslin">John G. Breslin</a>, <a href="/author/Passant">Alexandre Passant</a>, and <a href="/author/Decker">Stefan Decker</a> </span><em>The Social Semantic Web, </em><em>Springer, </em><em>Berlin u.a., </em>(<em>2009</em>)Sat Feb 11 19:24:16 CET 2012Berlin [u.a.]The Social Semantic Web137--158Social tagging2009Social Tagging Tagging has rapidly become a common and popular practice on social websites. It allows people to easily annotate the content they publish or share with free-form keywords in order to make the content more easily browsable and discoverable by others, leading to a social component of tagging. While tagging is a lightweight, agile and evolving way to annotate content, we believe it can be efficiently combined with formal modelling schemes such as ontologies to make it more powerful and to be part of the Semantic Web as a whole.