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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Discovering Opinion Spammer Groups by Network Footprints
verfasst von : Junting Ye, Leman Akoglu
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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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.