The paper “Friendship prediction and homophily in social media” has been accepted for publication on the ACM Transaction on the Web (TWEB) journal.
Abstract
Web 2.0 applications have attracted considerable 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 topical components of social media has been only partially explored. Here we study the presence of homophily in three systems that combine tagging of social media with online social networks. We find a substantial level of topical similarity 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 similarity 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 interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately.
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