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  • × theme_ss:"Social tagging"
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  1. Lüth, J.: Inhaltserschließung von Internetquellen durch Nutzerinnen und Nutzer : Ergebnisse eines Tests mit Internetquellen der Virtuellen Fachbibliothek EconBiz (2009) 0.00 
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    Abstract
    EconBiz, die Virtuelle Fachbibliothek Wirtschaftswissenschaften, wird gemeinsam von der Deutschen Zentralbibliothek für Wirtschaftswissenschaften (ZBW) und der Universitäts- und Stadtbibliothek Köln angeboten. Ein zentraler Bestandteil von EconBiz ist der Fachinformationsführer für Internetquellen, der derzeit über 16.000 Datensätze umfasst. Der Fachinformationsführer dient der Recherche nach Internetquellen und frei verfügbaren Volltexten. Die Erschließung dieser Quellen erfolgt arbeitsteilig durch bibliothekarisches und wissenschaftliches Personal im LinkShare System, einem System für die kooperative Erschließung und Verwaltung digitaler Netzobjekte. Nutzerinnen und Nutzer von EconBiz haben die Möglichkeit, Quellen für die Aufnahme in den Fachinformationsführer vorzuschlagen. Eine Beteiligung an der inhaltlichen Erschließung der Quellen erfolgt nicht. In Social-Bookmarking-Portalen wie "deLicio.us", "Furl" oder "Mister Wong" haben Nutzerinnen und Nutzer die Möglichkeit, eigene Listen mit Lesezeichen, englisch "Bookmarks", zu verwalten. Dazu bieten die verschiedenen Anbieter dieser Dienste allerhand nützliche Funktionen, u. a. mit folgenden Merkmalen: - Vergabe von Kategorien oder Schlagworten, so genannten "Tags", - Anlegen privater oder öffentlicher Bookmarks, - Abspeichern einer Version der Quelle. Derzeit üben diese Dienste eine sehr hohe Anziehungskraft auf Nutzerinnen und Nutzer aus, zum einen für die Verwaltung der eigenen Lesezeichen, zum anderen als Instrument für die Recherche nach relevanten Quellen. Mehr über das Potential und die Einsatzmöglichkeiten von Social-Bookmarking-Diensten möchte die ZBW im Rahmen eines Projekts "Inhaltserschließung durch Nutzerinnen und Nutzer" erfahren. Dafür wurde über einen begrenzten Zeitraum eine Teilmenge der im EconBiz-Fachinformationsführer enthaltenen Internetquellen in Social-Bookmarking-Webseiten angeboten. Ziel ist es, Erkenntnisse darüber zu gewinnen, in welchem Umfang diese von den Nutzerinnen und Nutzern der Socaial-Bookmarking-Dienste nachgenutzt und um eigene Schlagworte ergänzt werden.
    Type
    a
  2. Bar-Ilan, J.; Zhitomirsky-Geffet, M.; Miller, Y.; Shoham, S.: ¬The effects of background information and social interaction on image tagging (2010) 0.00 
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    Abstract
    In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the wisdom of the crowds helps overcome the difficulties due to the lack of information.
    Type
    a
  3. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.00 
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    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
    Type
    a
  4. Xu, C.; Zhang, Q.: ¬The dominant factor of social tags for users' decision behavior on e-commerce websites : color or text (2019) 0.00 
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    Abstract
    Colored Tags (abbr.Tag) as a unique type of social tags is used on e-commerce websites (e.g., Taobao) to summarize the high-frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e-commerce websites and other online platforms.
    Type
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  5. Bertram, J.: Social Tagging : zum Potential einer neuen Indexiermethode (2009) 0.00 
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  6. Voß, J.: Vom Social Tagging zum Semantic Tagging (2008) 0.00 
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  7. Held, C.; Cress, U.: Social Tagging aus kognitionspsychologischer Sicht (2008) 0.00 
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  8. Schiefner, M.: Social Tagging in der universitären Lehre (2008) 0.00 
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  9. Blank, M.; Bopp, T.; Hampel, T.; Schulte, J.: Social Tagging = Soziale Suche? (2008) 0.00 
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  10. Sack, H.; Waitelonis, J.: Zeitbezogene kollaborative Annotation zur Verbesserung der inhaltsbasierten Videosuche (2008) 0.00 
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  11. Tschetschonig, K.; Ladengruber, R.; Hampel, T.; Schulte, J.: Kollaborative Tagging-Systeme im Electronic Commerce (2008) 0.00 
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  12. Peters, I.; Schumann, L.; Terliesner, J.: Folksonomy-basiertes Information Retrieval unter der Lupe (2012) 0.00 
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  13. Stuart, E.: Flickr: organizing and tagging images online (2019) 0.00 
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  14. Peters, I.: Folksonomies & Social Tagging (2023) 0.00 
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  15. Derntl, M.; Hampel, T.; Motschnig, R.; Pitner, T.: Social Tagging und Inclusive Universal Access (2008) 0.00 
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  16. Panke, S.; Gaiser, B.: "With my head up in the clouds" : Social Tagging aus Nutzersicht (2008) 0.00 
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  17. Schillerwein, S.: ¬Der 'Business Case' für die Nutzung von Social Tagging in Intranets und internen Informationssystemen (2008) 0.00 
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  18. Birkenhake, B.: Semantic Weblog : Erfahrungen vom Bloggen mit Tags und Ontologien (2008) 0.00 
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  19. Güntner, G.; Sint, R.; Westenthaler, R.: ¬Ein Ansatz zur Unterstützung traditioneller Klassifikation durch Social Tagging (2008) 0.00 
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  20. Pammer, V.; Ley, T.; Lindstaedt, S.: tagr: Unterstützung in kollaborativen Tagging-Umgebungen durch semantische und assoziative Netzwerke (2008) 0.00 
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