Abstract of Talk
Towards Robust Trust Establishment in Online Communities with SocialTrust
Abstract: Web 2.0 promises rich opportunities for information sharing, electronic commerce, and new modes of social interaction, all centered around the "social Web" of user-contributed content, social annotations, and person-to-person social connections. But the increasing reliance on this "social Web" also places individuals and their computer systems at risk. In this talk, we identify a number of vulnerabilities inherent in online communities and study opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, we propose the SocialTrust framework for tamper-resilient trust establishment in online communities. Two of the salient features of SocialTrust are its dynamic revision of trust by (i) distinguishing relationship quality from trust; and (ii) incorporating a personalized feedback mechanism for adapting as the community evolves. We experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace proles and relationships. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants.
Biography: James Caverlee is an Assistant Professor of Computer Science at Texas A&M University. Dr. Caverlee directs the Web and Distributed Information Management Lab at Texas A&M and is also affiliated with the Center for the Study of Digital Libraries. At Texas A&M, Dr. Caverlee is leading research projects on (i) SocialTrust: Trusted Social Information Management; (ii) SpamGuard: Countering Spam and Deception on the Web; and (iii) Distributed Web Search, Retrieval, and Mining. Dr. Caverlee received his Ph.D. from Georgia Tech in 2007 (advisor: Ling Liu; co-advisor: William B. Rouse). Dr. Caverlee graduated magna cum laude from Duke University in 1996 with a B.A. in Economics. He received the M.S. degree in Engineering-Economic Systems & Operations Research in 2000, and the M.S. degree in Computer Science in 2001, both from Stanford University.