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 individuallevel 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