Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
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An Information-Theoretic Definition of Similarity
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The link prediction problem for social networks
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Lexical and semantic clustering by web links
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Usage patterns of collaborative tagging systems
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HT06, tagging paper, taxonomy, Flickr, academic article, to read
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A scalable, collaborative similarity measure for social annotation systems
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Friendship, collaboration and semantics in Flickr: from social interaction to semantic similarity
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Learning algorithms for the classification restricted Boltzmann machine
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Effective next-items recommendation via personalized sequential pattern mining
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Tripartite community structure in social bookmarking data
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Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.