Understanding latent interactions in online social networks

  • Authors:

  • Jing Jiang;Christo Wilson;Xiao Wang;Peng Huang;Wenpeng Sha;Yafei Dai;Ben Y. Zhao

  • Affiliations:

  • Peking University and U. C. Santa Barbara, Peking, China, China;U. C. Santa Barbara, Santa Barbara, CA, USA;Peking University, Peking, China, China;Peking University, Peking, China, China;Peking University, Peking, China, China;Peking University, Peking, China, China;U. C. Santa Barbara, Santa Barbara, CA, USA

  • Venue:
  • IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
  • Year:
  • 2010

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Abstract

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.