Search (14 results, page 1 of 1)

  • × theme_ss:"Social tagging"
  • × year_i:[2010 TO 2020}
  1. Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010) 0.01 
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  2. Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012) 0.01 
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    Abstract
    This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
  3. Xu, C.; Ma, B.; Chen, X.; Ma, F.: Social tagging in the scholarly world (2013) 0.01 
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    Abstract
    The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.
  4. Heuwing, B.: Erfahrungen an der Universitätsbibliothek Hildesheim : Social Tagging in Bibliotheken (2010) 0.01 
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  5. Golbeck, J.; Koepfler, J.; Emmerling, B.: ¬An experimental study of social tagging behavior and image content (2011) 0.01 
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  6. Stvilia, B.; Jörgensen, C.: Member activities and quality of tags in a collection of historical photographs in Flickr (2010) 0.00 
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  7. Niemann, C.: Intelligenz im Chaos : erste Schritte zur Analyse des Kreativen Potenzials eines Tagging-Systems (2010) 0.00 
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    Abstract
    Die Auszeichnung digitaler Medien durch Tagging ist zur festen Größe für das Wissensmanagement im Internet avanciert. Im Kontext des zunehmenden information overload' stehen wissenschaftliche Bibliotheken vor der Aufgabe, die große Flut digital publizierter Artikel und Werke möglichst inhaltlich erschlossen verfügbar zu machen. Die Frage ist, ob durch den Einsatz von Tagging-Systemen die kollaborative Intelligenz der NutzerInnen für die Sacherschließung eingesetzt werden kann, während diese von einer intuitiven und individuellen Wissensorganisation profitieren. Die große Freiheit bei der Vergabe von Deskriptoren durch die NutzerInnen eines Tagging-Systems ist nämlich ein ambivalentes Phänomen: Kundennähe und kreatives Potenzial stehen der großen Menge völlig unkontrollierter Meta-Informationen gegenüber, deren inhaltliche Qualität und Aussagekraft noch unklar ist. Bisherige Forschungsbemühungen konzentrieren sich hauptsächlich auf die automatische Hierarchisierung bzw. Relationierung der Tag-Daten (etwa mittels Ähnlichkeitsalgorithmen) oder auf die Analyse des (Miss-)Erfolgs, den die NutzerInnen bei einer Suchanfrage subjektiv erfahren. Aus der Sicht stark strukturierter Wissensorganisation, wie sie Experten z. B. durch die Anwendung von Klassifikationen realisieren, handelt es sich bei den zunächst unvermittelt nebeneinander stehenden Tags allerdings kurz gesagt um Chaos. Dass in diesem Chaos aber auch Struktur und wertvolles Wissen als Gemeinschaftsprodukt erzeugt werden kann, ist eine der zentralen Thesen dieses Artikels.
  8. 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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  9. Estrada, L.M.; Hildebrand, M.; Boer, V. de; Ossenbruggen, J. van: Time-based tags for fiction movies : comparing experts to novices using a video labeling game (2017) 0.00 
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    Abstract
    The cultural heritage sector has embraced social tagging as a way to increase both access to online content and to engage users with their digital collections. In this article, we build on two current lines of research. (a) We use Waisda?, an existing labeling game, to add time-based annotations to content. (b) In this context, we investigate the role of experts in human-based computation (nichesourcing). We report on a small-scale experiment in which we applied Waisda? to content from film archives. We study the differences in the type of time-based tags between experts and novices for film clips in a crowdsourcing setting. The findings show high similarity in the number and type of tags (mostly factual). In the less frequent tags, however, experts used more domain-specific terms. We conclude that competitive games are not suited to elicit real expert-level descriptions. We also confirm that providing guidelines, based on conceptual frameworks that are more suited to moving images in a time-based fashion, could result in increasing the quality of the tags, thus allowing for creating more tag-based innovative services for online audiovisual heritage.
  10. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.00 
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  11. Niemann, C.: Tag-Science : Ein Analysemodell zur Nutzbarkeit von Tagging-Daten (2011) 0.00 
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    Source
    ¬Die Kraft der digitalen Unordnung: 32. Arbeits- und Fortbildungstagung der ASpB e. V., Sektion 5 im Deutschen Bibliotheksverband, 22.-25. September 2009 in der Universität Karlsruhe. Hrsg: Jadwiga Warmbrunn u.a
  12. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.00 
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    Date
    25.12.2012 15:22:37
  13. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.00 
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    Date
    21. 4.2016 11:23:22
  14. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.00 
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    Date
    20. 1.2015 18:30:22

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