Opinion Fraud Detection in Online Reviews by Network Effects

TLDR

User‑generated online reviews influence product success, yet they are frequently targeted by opinion spammers who distort perceived quality. This study proposes FRAUDEAGLE, a fast, effective framework for detecting fraudsters and fake reviews in online review datasets. FRAUDEAGLE exploits network effects among reviewers and products, scores users and reviews for fraud detection, groups them for visualization, operates unsupervised without labeled data, and scales linearly with network size. On synthetic and real datasets, FRAUDEAGLE successfully identified fraud bots in a large online app review database.

Abstract

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.