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2015
Springer

Discovering Opinion Spammer Groups by Network Footprints

3 years 11 months ago
Discovering Opinion Spammer Groups by Network Footprints
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.
Junting Ye, Leman Akoglu
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Junting Ye, Leman Akoglu
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