Discovering Opinion Spammer Groups by Network Footprints

• Published in: Machine Learning and Knowledge Discovery in Databases pp. 267 - 282
• Main Authors: Ye, Junting, Akoglu, Leman
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: Efficient hierarchical clustering; Graph anomaly detection; Network footprints; Opinion spam; Spam detection; Spammer groups
• ISBN: 3319235273; 9783319235271
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-23528-8_17

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. 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.