  Product reviews are extremely valuable for online shoppers in providing
purchase decisions. Driven by immense profit incentives, fraudsters
deliberately fabricate untruthful reviews to distort the reputation of online
products. As online reviews become more and more important, group spamming,
i.e., a team of fraudsters working collaboratively to attack a set of target
products, becomes a new fashion. Previous works use review network effects,
i.e. the relationships among reviewers, reviews, and products, to detect fake
reviews or review spammers, but ignore time effects, which are critical in
characterizing group spamming. In this paper, we propose a novel Markov random
field (MRF)-based method (ColluEagle) to detect collusive review spammers, as
well as review spam campaigns, considering both network effects and time
effects. First we identify co-review pairs, a review phenomenon that happens
between two reviewers who review a common product in a similar way, and then
model reviewers and their co-review pairs as a pairwise-MRF, and use loopy
belief propagation to evaluate the suspiciousness of reviewers. We further
design a high quality yet easy-to-compute node prior for ColluEagle, through
which the review spammer groups can also be subsequently identified.
Experiments show that ColluEagle can not only detect collusive spammers with
high precision, significantly outperforming state-of-the-art baselines ---
FraudEagle and SpEagle, but also identify highly suspicious review spammer
campaigns.
