Hidden location prediction using check-in patterns in location-based social networks

Online product reviews nowadays are increasingly prevalent in E-commerce websites. People often refer to product reviews to evaluate the quality of a product before purchasing. However, there have been a large number of review spammers who often work collaboratively to promote or demote target products, which severely harm the review system. Much previous work exploits machine learning approaches to detect suspicious reviews/reviewers. In this paper, we introduce a top-down computing framework, namely GGSpam, to detect review spammer groups by exploiting the topological structure of the underlying reviewer graph which reveals the co-review collusiveness. A novel instantiation of GGSpam, namely GSBC, is designed by modeling spammer groups as bi-connected graphs. Given a reviewer graph, GSBC identifies all the bi-connected components whose spamicity scores exceed the given spam threshold. For large unsuspicious bi-connected graphs, the minimum cut algorithm is used to split the graph, and the smaller graphs are further processed recursively. A variety of group spam indicators are designed to measure the spamicity of a spammer group. Experimental study shows that the proposed approach is both effective and efficient and outperforms several state-of-the-art baselines, including graph based and non-graph based, by a large margin.