An epistemic dynamic model for tagging systems

Dellschaft, Klaas; Staab, Steffen

doi:10.1145/1379092.1379109

Cited by 48 publications

(36 citation statements)

References 7 publications

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“…Several researchers have taken these opportunities to study tag frequency distribution at different levels. Most researchers who examined the distribution of tag frequencies for a given resource (DELLSCHAFT; STAAB, 2008;GOLDER;HUBERMAN, 2006;GUY;TONKIN, 2006;ROBU;SHEPHERD, 2007) find that the distribution looks like a power law curve, which means that there is usually a small number of tags that are extremely popular at the beginning of the distribution, followed by a sharp drop and a long tail of tags that have been applied very rarely. Levy and Sandler (2009) also detect the same type of distribution when examining tag frequencies at the database level in Last.fm and MyStrands.…”

Section: Social Tag Frequency Distributionmentioning

confidence: 99%

Tagged at first listen: an examination of social tagging practices in a music recommender system

Laplante

2015

Enc. Bibli: R. Eletr. Bibliotecon. Ci. Inf.

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Social tagging has become a very common way to index different types of resources on the web. Less prevalent in music than in other domains, social tagging is nevertheless used in a popular recommender system, Last.fm. Although the number of publications on tagging and folksonomies has exploded in the last few years, music tagging is still not well studied. In this paper, we present a study of tagging practices of Last.fm users. We examine the social tagging of songs during the first three months after their release. Our analysis shows that the release of a song triggers a burst in tagging activity that lasts two weeks, after what it decreases sharply and then remains fairly constant for the next ten weeks. We also find that a majority of songs do not get tagged during the first week and that tagging was positively related to popularity. Finally, we find that tags that have been frequently applied to a given song are more likely to be genre related, shorter in length, and relatively objective than tags that have been applied only once.Keywords: Social tagging; music indexing; music recommender systems Resumo A etiquetagem social (social tagging) tornou-se uma forma muito comum de indexar diferentes tipos de recursos na web. Menos predominante na música do que em outros domínios, a etiquetagem social é utilizada em um popular sistema de recomendação, Last.fm. Embora o número de publicações sobre atribuição de tags (etiquetas) e folksonomia tenha explodido nos últimos anos, a atribuição de tags à música permanece pouco estudada. Neste artigo, apresentamos um estudo das práticas de atribuição de tags dos usuários do Last.fm. Examinamos a etiquetagem social de músicas durante os primeiros três meses após sua liberação. Nossa análise mostra que a liberação de uma música desencadeia uma explosão nas atividades de atribuição de tags que dura duas semanas, depois essa atividade diminui de forma acentuada e, então, permanece razoavelmente constante nas próximas 10 semanas. Também verificamos que a maioria das músicas não recebem tags durante a primeira semana e que a atribuição de tags foi positivamente relacionada à popularidade. Finalmente, constatamos que as tags que são frequentemente aplicadas a determinada música são mais relacionadas ao gênero, são menores em extensão e relativamente mais objetivas do que tags que tenham sido aplicadas uma única vez.Palavras-chave: Etiquetagem social; tag; indexação de música; sistemas de recomendação de música Esta obra está licenciada sob uma Licença Creative Commons.

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Section: Social Tag Frequency Distributionmentioning

confidence: 99%

Tagged at first listen: an examination of social tagging practices in a music recommender system

Laplante

2015

Enc. Bibli: R. Eletr. Bibliotecon. Ci. Inf.

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“…Twitter provides a useful set of distinctions because the ways tags can be used are differently constrained. Dellschaft and Staab [4] looked at tag stream data and ranked tags by frequency of use to understand how individual users made tag selections. It is uncommon for a tweet to be assigned more than one tag, so we do not compare tag co-occurrence; instead, we look at tag selection from a social context since Twitter users are influenced by the tags used by people in their network or from lists of trending topics, when they choose tags according to the 'Imitation' model proposed by Dellschaft and Staab.…”

Section: Related Workmentioning

confidence: 99%

Conversational tagging in twitter

Huang

Thornton

Efthimiadis

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

208

144

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Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

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“…A research stream related to collaborative tagging is concerned about viewing social tags as indexing terms, including the analysis of tags in syntactic structure, linguistic function, etc (Kipp & Campbell, 2006;Spiteri, 2007), the examination of tags across different tagging systems (Heckner, Muhlbacher, & Wolff, 2007), and the attempt to link tags to controlled vocabularies such as Library of Congress Subject Headings (Yi & Chan, 2009). Some theoretic models were attempts to describe social tagging activities: basic Polya Urn model (Golder & Huberman, 2006) for tag generation, YuleSimon model (Cattuto, Baldassarri, Servedio, & Loreto, 2007) for the growth of tags, and power law (Dellschaft & Staab, 2008) for tag frequency-rank distribution. Nevertheless, there are only a few studies, including (Yi, 2009a), that attempt to directly quantify and measure the value of social tags as indexing terms.…”

Section: Related Workmentioning

confidence: 99%

An empirical study on the automatic resolution of semantic ambiguity in social tags

2011

Proc. Am. Soc. Info. Sci. Tech.

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Due to the popularity of collaborative tagging services and systems, the role of social tags in information organization and retrieval has become increasingly critical within the tagging system. Consequently, the resolution of semantic ambiguity of tag sense (i.e., meaning) plays an important role in the enhancement of information organization and retrieval in collaborative tagging applications. Our approach to tackle the task of automatic resolution of semantic ambiguity is based on the hypothesis that given a target tag, some of the co-occurring social tags that were selectively assigned by the same or by different people to the same resource can serve as a useful dataset. Four different methods (i.e., two LSA-based, LIN-based, and Cooccurrence method) for tag sense disambiguation are proposed and their results are compared, aiming to automatically rank the senses associated with a given target tag. The experimental results with a Delicious dataset indicate that LSA_w and LIN-based methods produce more stable performances, and suggest the same two methods for the Tag Sense Disambiguation task, particularly in the situation that the Boolean effect is more likely to arise. KeywordsTag sense disambiguation, latent semantic analysis, Meanings of tag.

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An epistemic dynamic model for tagging systems

Cited by 48 publications

References 7 publications

Tagged at first listen: an examination of social tagging practices in a music recommender system

Tagged at first listen: an examination of social tagging practices in a music recommender system

Conversational tagging in twitter

An empirical study on the automatic resolution of semantic ambiguity in social tags

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