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 un- supervised 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.
The rise of social media has led to an explosion in the number of possible sites users can join. However, this same profusion of social media sites has made it nearly impossible for users to actively engage in all of them simultaneously. Accordingly, users must make choices about which sites to use or to neglect. In this paper, we study users that have joined multiple sites. We study how individuals are distributed across sites, the way they select sites to join, and behavioral patterns they exhibit while selecting sites. Our study demonstrates that while users have a tendency to join the most popular or trendiest sites, this does not fully explain users' selections. We demonstrate that peer pressure also influences the decisions users make about joining emerging sites.  In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to and interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks and we apply the model to a real dataset extracted from two microblogging sites.  The incredible growth of the social web over the last decade has ushered in a flurry of new social media sites. On one hand, users have an inordinate number of choices; on the other hand, users are constrained by limited time and resources and have to choose sites in order to remain social and active. Hence, dynamic social media entails user migration, a well studied phenomenon in fields such as sociology and psychology. Users are valuable assets for social media sites as they help contribute to the growth of a site and generate revenue by increased traffic. We are intrigued to know if social media user migration can be studied, and what migration patterns are. In particular, we investigate whether people migrate, and if they do, how they migrate. We formalize site and attention migration to help identify the migration between popular social media sites and determine clear patterns of migration between sites. This work suggests a feasible way to study migration patterns in social media. The discovered patterns can help understand social media sites and gauge their popularity to improve business intelligence and revenue generation through the retention of users. People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better profile of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identifies users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. We formally define the cross-media user identification problem and show that MOBIUS is effective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.