Detecting Collusive Cheating in Online Shopping Systems through Characteristics of Social Networks

Detecting the collaborative cheating in an online shopping system is an important but challenging issue. In this paper, we propose a novel approach to detect the collusive manipulation on ratings in Amazon, an online shopping system. Rather than focusing on rating values, we believe the online shopping and rating activities have nontrivial attributes in terms of social network connections. Our major contributions include: (a) We build a virtual social network based on users' ratings and comments, and detect the collusive cheating based on the social network activities. (b) We investigate the properties of disconnected components in a wide range of social networks, such as the longevity and final size of the disconnected components before they join the giant connected component or merge with other disconnected components. (c) We apply our proposed collusion detection algorithm to detect the possible collusive cheating on the ratings based on the data we crawl from Amazon, and the experimental results validate our approach.