Title
Friendship prediction and homophily in social media
Abstract
Social media 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 social media with online social networks. We find a substantial level of topical similarity among users who are 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. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92%.
Year
DOI
Venue
2012
10.1145/2180861.2180866
TWEB
Keywords
Field
DocType
social network,friendship prediction,topical similarity,topical similarity measure,social media,online social network,actual local similarity,user activity,social link,actual friendship,topical component,null model
Assortative mixing,Metadata,Annotation,Social media,Social network,Information retrieval,Friendship,Computer science,Homophily,Centrality
Journal
Volume
Issue
ISSN
6
2
1559-1131
Citations 
PageRank 
References 
54
1.77
41
Authors
6
Name
Order
Citations
PageRank
Luca Maria Aiello171344.77
Alain Barrat2140187.12
Rossano Schifanella361935.44
Ciro Cattuto4174097.27
Benjamin Markines550222.08
Filippo Menczer63874268.67