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  • × language_ss:"e"
  • × theme_ss:"Social tagging"
  • × year_i:[2010 TO 2020}
  1. Spiteri, L.F.: Extending the scope of library discovery systems via hashtags (2018) 0.00 
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    Source
    Challenges and opportunities for knowledge organization in the digital age: proceedings of the Fifteenth International ISKO Conference, 9-11 July 2018, Porto, Portugal / organized by: International Society for Knowledge Organization (ISKO), ISKO Spain and Portugal Chapter, University of Porto - Faculty of Arts and Humanities, Research Centre in Communication, Information and Digital Culture (CIC.digital) - Porto. Eds.: F. Ribeiro u. M.E. Cerveira
  2. Lee, D.H.; Schleyer, T.: Social tagging is no substitute for controlled indexing : a comparison of Medical Subject Headings and CiteULike tags assigned to 231,388 papers (2012) 0.00 
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    Abstract
    Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings (MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.9, S.1747-1757
  3. Kipp, M.E.; Beak, J.; Choi, I.: Motivations and intentions of flickr users in enriching flick records for Library of Congress photos (2017) 0.00 
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    Abstract
    The purpose of this study is to understand users' motivations and intentions in the use of institutional collections on social tagging sites. Previous social tagging studies have collected social tagging data and analyzed how tagging functions as a tool to organize and retrieve information. Many studies focused on the patterns of tagging rather than the users' perspectives. To provide a more comprehensive picture of users' social tagging activities in institutional collections, and how this compares to social tagging in a more personal context, we collected data from social tagging users by surveying 7,563 participants in the Library of Congress's Flickr Collection. We asked users to describe their motivations for activities within the LC Flickr Collection in their own words using open-ended questions. As a result, we identified 11 motivations using a bottom-up, open-coding approach: affective reactions, opinion on photo, interest in subject, contribution to description, knowledge sharing, improving findability, social network, appreciation, personal use, and personal relationship. Our study revealed that affective or emotional reactions play a critical role in the use of social tagging of institutional collections by comparing our findings to existing frameworks for tagging motivations. We also examined the relationships between participants' occupations and our 11 motivations.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2364-2379
  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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    Footnote
    Beitrag in einem 'Special issue on neuro-information science'.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.9, S.942-953
  5. Rafferty, P.: Tagging (2018) 0.00 
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    Abstract
    This article examines tagging as knowledge organization. Tagging is a kind of indexing, a process of labelling and categorizing information made to support resource discovery for users. Social tagging generally means the practice whereby internet users generate keywords to describe, categorise or comment on digital content. The value of tagging comes when social tags within a collection are aggregated and shared through a folksonomy. This article examines definitions of tagging and folksonomy, and discusses the functions, advantages and disadvantages of tagging systems in relation to knowledge organization before discussing studies that have compared tagging and conventional library-based knowledge organization systems. Approaches to disciplining tagging practice are examined and tagger motivation discussed. Finally, the article outlines current research fronts.
  6. Antin, J.; Earp, M.: With a little help from my friends : self-interested and prosocial behavior on MySpace Music (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.952-963
  7. Raban, D.R.; Ronen, I.; Guy, I.: Acting or reacting? : Preferential attachment in a people-tagging system (2011) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.738-747
  8. Golbeck, J.; Koepfler, J.; Emmerling, B.: ¬An experimental study of social tagging behavior and image content (2011) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1750-1760
  9. Wang, Y.; Tai, Y.; Yang, Y.: Determination of semantic types of tags in social tagging systems (2018) 0.00 
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    Abstract
    The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.
  10. 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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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2477-2489
  11. 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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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
  12. 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) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162
  13. 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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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.348-364

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