GroupTie
Zhen Xie, Sencun Zhu · 2014
The current centralized application (or app) markets provide convenient ways to distribute mobile apps. Their vendors maintain rating systems, which allow customers to leave ratings and reviews. Since positive ratings and reviews can lead to more downloads/installations and hence more monetary benefit, the rating systems have become a target of manipulation by some collusion groups hired by app developers. In this paper, we thoroughly analyze the features of hidden collusion groups and propose a novel method called \emph{GroupTie} to narrow down the suspect list of collusive reviewers for further investigation by app stores. As members of a hidden collusion group have to work together more frequently and their ratings often deviate more from apps' quality, collusive actions will enhance their relation over time. We build a relation graph named \emph{tie graph} and detect collusion groups by applying graph clustering. Simulation results show that the precision of GroupTie approaches to $99.70\%$ and the recall is about $91.50\%$. We also apply our method to detect hidden collusion groups among the reviewers of $89$ apps in Apple's China App Store. A large number of reviewers are discovered belonging to a large collusion group and several small groups.