  Review fraud is a pervasive problem in online commerce, in which fraudulent
sellers write or purchase fake reviews to manipulate perception of their
products and services. Fake reviews are often detected based on several signs,
including 1) they occur in short bursts of time; 2) fraudulent user accounts
have skewed rating distributions. However, these may both be true in any given
dataset. Hence, in this paper, we propose an approach for detecting fraudulent
reviews which combines these 2 approaches in a principled manner, allowing
successful detection even when one of these signs is not present. To combine
these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD)
model, a flexible Bayesian model of user rating behavior. Based on our model we
formulate a likelihood-based suspiciousness metric, Normalized Expected
Surprise Total (NEST). We propose a linear-time algorithm for performing
Bayesian inference using our model and computing the metric. Experiments on
real data show that BIRDNEST successfully spots review fraud in large,
real-world graphs: the 50 most suspicious users of the Flipkart platform
flagged by our algorithm were investigated and all identified as fraudulent by
domain experts at Flipkart.
