Folks in Folksonomies: Social Link Prediction from Shared Metadata

TL;DR

This paper investigates how shared metadata and semantic similarity among users in social media platforms like Flickr and Last.fm can predict social links, revealing that users with similar interests tend to be connected.

Contribution

It introduces a null model to distinguish genuine local semantic alignment from statistical effects, demonstrating that semantic similarity can effectively predict social links.

Findings

Semantic similarity correlates with social connections.

Users with similar interests are more likely to be friends.

Semantic-based social network prediction outperforms listening pattern methods.

Abstract

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.