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 study on structural properties and user behaviors in multiple OSNs is an emerging topic and the research subject has been gaining attractions in recent years. Magnani and Rossi @cite_5 did a study on the structural properties in multiple OSNs and proposed to represent multiple OSNs as a . They had also extended the degree and closeness centrality measures to multi-layer network. Their work however did not consider other network structural properties or behaviors such as the friendship similarity and evenness across networks. The linkage of user accounts across multiple OSNs belong to the same person is also a widely studied topic @cite_17 @cite_10 . With wider adoption of the new user linkage methods by proposed by previous research works, researchers also studied user behaviors across multiple OSNs. Benevenuto, et. al, performed a macro-level analysis of user behaviors such as browsing and content posting at different OSNs @cite_14 . Zafarani and Liu conducted an empirical study on users in 20 social media sites and showed that the most users join and stay active in less than 3 social media sites @cite_18 . analyzed the user migration patterns across seven OSNs @cite_20 .
@cite_18 @cite_14 @cite_5 @cite_10 @cite_20 @cite_17
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In the last decade, Online Social Networks (OSNs) have taken the world by storm. They range from superficial to professional, from focused to general-purpose, and, from free-form to highly structured. Numerous people have multiple accounts within the same OSN and even more people have an account on more than one OSN. Since all OSNs involve some amount of user input, often in written form, it is natural to consider whether multiple incarnations of the same person in various OSNs can be effectively correlated or linked. One intuitive means of linking accounts is by using stylometric analysis. This paper reports on (what we believe to be) the first trilateral large-scale stylometric OSN linkability study. Its outcome has important implications for OSN privacy. The study is trilateral since it involves three OSNs with very different missions: (1) Yelp, known primarily for its user-contributed reviews of various venues, e.g, dining and entertainment, (2) Twitter, popular for its pithy general-purpose micro-blogging style, and (3) Flickr, used exclusively for posting and labeling (describing) photographs. As our somewhat surprising results indicate, stylometric linkability of accounts across these heterogeneous OSNs is both viable and quite effective. The main take-away of this work is that, despite OSN heterogeneity, it is very challenging for one person to maintain privacy across multiple active accounts on different OSNs. Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior, and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, passive actions such as profile browsing that cannot be observed by traditional measurement techniques. In this paper, we seek a deeper understanding of both visible and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 150 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed visitor logs for each user profile, and counters for each photo and diary blog entry. We capture detailed histories of profile visits over a period of 90 days for more than 61,000 users in the Peking University Renren network, and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than visible events, non-reciprocal in nature, and that profile popularity are uncorrelated with the frequency of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior, and compare their structural properties against those of both visible interaction graphs and social graphs. Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small. Various online social networks (OSNs) have been developed rapidly on the Internet. Researchers have analyzed different properties of such OSNs, mainly focusing on the formation and evolution of the networks as well as the information propagation over the networks. In knowledge-sharing OSNs, such as blogs and question answering systems, issues on how users participate in the network and how users "generate contribute" knowledge are vital to the sustained and healthy growth of the networks. However, related discussions have not been reported in the research literature. In this work, we empirically study workloads from three popular knowledge-sharing OSNs, including a blog system, a social bookmark sharing network, and a question answering social network to examine these properties. Our analysis consistently shows that (1) users' posting behavior in these networks exhibits strong daily and weekly patterns, but the user active time in these OSNs does not follow exponential distributions; (2) the user posting behavior in these OSNs follows stretched exponential distributions instead of power-law distributions, indicating the influence of a small number of core users cannot dominate the network; (3) the distributions of user contributions on high-quality and effort-consuming contents in these OSNs have smaller stretch factors for the stretched exponential distribution. Our study provides insights into user activity patterns and lays out an analytical foundation for further understanding various properties of these OSNs.