- SSMS 1009
A deep understanding of user social interaction in social networking sites (SNSs) can provide important insights into questions of human social and relational behavior, as well as shape the design of future online social platforms and applications.
This talk presents a new data mining technique to capture natural social interaction in social network sites that offers a unique view of both “visible” (e.g., comments and wall posts) and “latent” (e.g., passive profile browsing) user social interactions in SNSs at a scale that has been heretofore unobservable with available research methods. Using this technique, social interactions from 42,115,509 users of Renren, the most popular SNS in China and a Facebook clone, were collected. The data show some surprising results that contradict most previous accounts of social behavior in SNSs and, as such, offer new insights into established areas of social scientific research including studies of interpersonal electronic surveillance (i.e., “lurking”) and social capital.
In addition to discussing our results, this talk will also highlight ways in which interdisciplinary collaboration between social and computer scientists is breaking new methodological ground and can provide an unprecedented wealth of naturalistic data for researchers studying human communication and social interaction processes.
Presenters: Miriam Metzger, Christo Wilson, and Ben Zhao. Metzger is a faculty member in Communication, while Wilson is a graduate student and Zhao is a faculty member in Computer Science.