Understanding Latent Interactions in Online Social Networks
Ben Y. Zhao
ACM Transactions on the Web (TWEB), Vol. 7, No. 4, October 2013.
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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, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques.
In this article, we seek a deeper understanding of both active 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 220 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, publicly viewable visitor
logs for each user profile. We capture detailed histories of profile
visits over a period of 90 days for 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 active events, are
nonreciprocal in nature, and that profile popularity is correlated with
page views of content rather than with quantity of content updates.
Finally, we construct latent interaction graphs as models of user
browsing behavior and compare their structural properties, evolution,
community structure, and mixing times against those of both active
interaction graphs and social graphs.