Conference Proceeding Article
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.
Multiple Social Networks, Twitter, Instagram, Link Prediction
Computer Sciences | Databases and Information Systems | Social Media
Data Management and Analytics
HT'16: Proceedings of the 27th ACM Conference on Hypertext and Social Media: July 10-13, 2016, Halifax, Nova Scotia, Canada
City or Country
LEE, Roy Ka Wei and LIM, Ee-Peng.
Friendship maintenance and prediction in multiple social networks. (2016). HT'16: Proceedings of the 27th ACM Conference on Hypertext and Social Media: July 10-13, 2016, Halifax, Nova Scotia, Canada. 83-92. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3291
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.