Discovering Opinion Spammer Groups by Network Footprints

Abstract

Online reviews are an important source for consumers to evaluate products/services on the Internet (e.g. Amazon, Yelp, etc.). However, more and more fraudulent reviewers write fake reviews to mislead users. To maximize their impact and share effort, many spam attacks are organized as campaigns, by a group of spammers. In this paper, we propose a new two-step method to discover spammer groups and their targeted products. First, we introduce NFS (Network Footprint Score), a new measure that quantifies the likelihood of products being spam campaign targets. Second, we carefully devise GroupStrainer to cluster spammers on a 2-hop subgraph induced by top ranking products. Our approach has four key advantages: (i) unsupervised detection; both steps require no labeled data, (ii) adversarial robustness; we quantify statistical distortions in the review network, of which spammers have only a partial view, and avoid any side information that spammers can easily evade, (iii) sensemaking; the output facilitates the exploration of the nested hierarchy (i.e., organization) among the spammers, and finally (iv) scalability; both steps have complexity linear in network size, moreover, GroupStrainer operates on a carefully induced subnetwork. We demonstrate the efficiency and effectiveness of our approach on both synthetic and real-world datasets from two different domains with millions of products and reviewers. Moreover, we discover interesting strategies that spammers employ through case studies of our detected groups.