Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2016

Abstract

Due to the proliferation of online social networks (OSNs), users find themselves participating in multiple OSNs. These users leave their activity traces as they maintain friendships and interact with other users in these OSNs. In this work, we analyze how users maintain friendship in multiple OSNs by studying users who have accounts in both Twitter and Instagram. Specifically, we study the similarity of a user's friendship and the evenness of friendship distribution in multiple OSNs. Our study shows that most users in Twitter and Instagram prefer to maintain different friendships in the two OSNs, keeping only a small clique of common friends in across the OSNs. Based upon our empirical study, we conduct link prediction experiments to predict missing friendship links in multiple OSNs using the neighborhood features, neighborhood friendship maintenance features and cross-link features. Our link prediction experiments shows that unsupervised methods can yield good accuracy in predicting links in one OSN using another OSN data and the link prediction accuracy can be further improved using supervised method with friendship maintenance and others measures as features.

Keywords

Multiple Social Networks, Twitter, Instagram, Link Prediction

Discipline

Computer Sciences | Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

HT'16: Proceedings of the 27th ACM Conference on Hypertext and Social Media: July 10-13, 2016, Halifax, Nova Scotia, Canada

First Page

83

Last Page

92

ISBN

9781450342476

Identifier

10.1145/2914586.2914593

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Additional URL

https://doi.org/10.1145/2914586.2914593

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