BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

TL;DR

This paper introduces BIRDNEST, a Bayesian model that effectively detects fake reviews by combining temporal and rating distribution signs, even when only one is present, demonstrated on real-world data.

Contribution

It presents a novel Bayesian inference approach that integrates multiple fraud indicators for robust review fraud detection.

Findings

Successfully identified fraudulent users on Flipkart

High accuracy in detecting review fraud in large datasets

Domain experts confirmed the flagged users as fraudulent

Abstract

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.