Spotting Suspicious Reviews via (Quasi-)clique Extraction

TL;DR

This paper introduces a clique-based method to identify suspicious and coordinated reviewers in online review platforms, revealing organized fraudulent activities and paid reviewers through graph analysis of review behaviors.

Contribution

It presents a novel clique extraction approach to detect well-organized suspicious reviewers, including paid Yelp Scouts, from large-scale review data.

Findings

Detected large cliques of coordinated reviewers

Identified paid Yelp Scouts among suspicious groups

Revealed organized fraudulent review activities

Abstract

How to tell if a review is real or fake? What does the underworld of fraudulent reviewing look like? Detecting suspicious reviews has become a major issue for many online services. We propose the use of a clique-finding approach to discover well-organized suspicious reviewers. From a Yelp dataset with over one million reviews, we construct multiple Reviewer Similarity graphs to link users that have unusually similar behavior: two reviewers are connected in the graph if they have reviewed the same set of venues within a few days. From these graphs, our algorithms extracted many large cliques and quasi-cliques, the largest one containing a striking 11 users who coordinated their review activities in identical ways. Among the detected cliques, a large portion contain Yelp Scouts who are paid by Yelp to review venues in new areas. Our work sheds light on their little-known operation.