  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.
