Folks in folksonomies: Social link prediction from shared metadata

Rossano Schifanella (1) , Alain Barrat (2, 3, 4) , Ciro Cattuto (5) , Benjamin Markines (6) , Filippo Menczer (5, 6)

1 UNITO - Università degli studi di Torino = University of Turin
2 CPT - E5 Physique statistique et systèmes complexes
3 CPT - Centre de Physique Théorique - UMR 6207
4 CPT - Centre de Physique Théorique - UMR 7332
5 ISI - Data Science Laboratory
6 School of Informatics and Computing, Indiana University [Bloomington]

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.

Domaines: Physique [physics]

Rossano Schifanella, Alain Barrat, Ciro Cattuto, Benjamin Markines, Filippo Menczer. Folks in folksonomies: Social link prediction from shared metadata. third ACM international conference on Web search and data mining, Feb 2010, New York, United States. pp.271, ⟨10.1145/1718487.1718521⟩. ⟨hal-00429886⟩