Social Trust and Reputation in Online Social Networks

Online social networking systems are rapidly becoming popular on the Internet for users to share, organize and locate interesting content. However, these systems have increasingly been employed as ideal platforms to spread spam and irrelevant content, abusing the valuable human attention and service resource. We propose a social reputation model to guide users to browse the desirable content. First, we compute the statistical correlation between different users to distinguish various user interests, then, since a user´s friends are usually trustworthy and share the similar interest, we further exploit the inherent friend relationships to perform the reliable social enhancements of vote history extension and efficient reputation estimation. In addition to providing a strong incentive for user cooperation, our model can handle the practical problems of inactive users, unpopular content and Sybil attacks effectively and efficiently. Our evaluation on a large-scale realistic network validates our analysis, and shows that our social reputation model can help users find the desirable content in various scenarios with a precision of around 94%.