Spotting Collective Behaviour of Online Frauds in Customer Reviews

Spotting Collective Behaviour of Online Frauds in Customer Reviews

Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 245-251. https://doi.org/10.24963/ijcai.2019/35

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.
Keywords:
Agent-based and Multi-agent Systems: Trust and Reputation
Multidisciplinary Topics and Applications: Security and Privacy