Understanding Latent Interactions in Online Social Networks
Jing Jiang
Christo Wilson
Xiao Wang
Peng Huang
Yafei Dai
Ben Y. Zhao
Proceedings of The 10th ACM SIGCOMM Internet Measurement Conference (IMC 2010)
[Full Text in PDF Format, 778KB]
Paper 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.