The SocialTrust framework for trusted social information management: Architecture and algorithms

  • Authors:

  • James Caverlee;Ling Liu;Steve Webb

  • Affiliations:

  • Department of Computer Science, Texas A&M University, TAMU 3112, College Station, TX 77843-3112, USA;College of Computing, Georgia Tech Atlanta, GA 30332-0765, USA;College of Computing, Georgia Tech Atlanta, GA 30332-0765, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

Social information systems are a promising new paradigm for large-scale distributed information management, as evidenced by the success of large-scale information sharing communities, social media sites, and web-based social networks. But the increasing reliance on these social systems also places individuals and their computer systems at risk, creating opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, this manuscript presents the SocialTrust framework for enabling trusted social information management in Internet-scale social information systems. Concretely, we study online social networks, consider a number of vulnerabilities inherent in online social networks, and introduce the SocialTrust framework for supporting tamper-resilient trust establishment. We study three key factors for trust establishment in online social networks - trust group feedback, distinguishing the user's relationship quality from trust, and tracking user behavior - and describe a principled approach for assessing each component. In addition to the SocialTrust framework, which provides a network-wide perspective on the trust of all users, we describe a personalized extension called mySocialTrust, which provides a user-centric trust perspective that can be optimized for individual users within the network. Finally, we experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace profiles and relationships. While other trust aggregation approaches have been developed and implemented by others, we note that it is rare to find such a large-scale experimental evaluation that carefully considers the important factors impacting the trust framework. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants.