# Spotting Collective Behaviour of Online Frauds in Customer Reviews

## TL;DR

This paper introduces DeFrauder, an unsupervised approach for detecting groups of online review spammers by analyzing review graph structures and behavioral signals, outperforming existing methods.

## Contribution

The paper presents a novel unsupervised method for group spam detection in online reviews, addressing challenges like unclear group definitions and limited labeled data.

## Key findings

- DeFrauder outperforms five baselines with 17.11% higher NDCG@50 on average.
- It effectively detects fraud groups by combining graph analysis and behavioral signals.
- The method is validated on four real-world datasets, including two curated by the authors.

## Abstract

Online reviews play a crucial role in deciding the quality before purchasing any product. Unfortunately, spammers often take advantage of online review forums by writing fraud reviews to promote/demote certain products. It may turn out to be more detrimental when such spammers collude and collectively inject spam reviews as they can take complete control of users' sentiment due to the volume of fraud reviews they inject. Group spam detection is thus more challenging than individual-level fraud detection due to unclear definition of a group, variation of inter-group dynamics, scarcity of labeled group-level spam data, etc. Here, we propose DeFrauder, an unsupervised method to detect online fraud reviewer groups. It first detects candidate fraud groups by leveraging the underlying product review graph and incorporating several behavioral signals which model multi-faceted collaboration among reviewers. It then maps reviewers into an embedding space and assigns a spam score to each group such that groups comprising spammers with highly similar behavioral traits achieve high spam score. While comparing with five baselines on four real-world datasets (two of them were curated by us), DeFrauder shows superior performance by outperforming the best baseline with 17.11% higher NDCG@50 (on average) across datasets.