Folks in Folksonomies: Social Link Prediction from Shared Metadata

Details Folks in Folksonomies: Social Link Prediction from Shared Metadata Folks in Folksonomies: Social Link Prediction from Shared Metadata

2010-03-11

arxiv.org/abs/1003.2281v1

Proceedings of the third ACM international conference on Web search and data mining WSDM2010, New York, Feb 4-6 2010, p. 271

Computer Science

Computers and Society

http://portal.acm.org/citation.cfm?doid=1718487.1718521

Scientific paper

10.1145/1718487.1718521

Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight 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.

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