  Online review systems are important components in influencing customers'
purchase decisions. To manipulate a product's reputation, many stores hire
large numbers of people to produce fake reviews to mislead customers. Previous
methods tackle this problem by detecting malicious individuals, ignoring the
fact that the spam activities are often formed in groups, where individuals
work collectively to write fake reviews. Fake reviewer group detection,
however, is more challenging due to the difficulties in capturing the
underlying relationships in groups. In this work, we present an unsupervised
and end-to-end approach for fake reviewer group detection in online reviews.
Specifically, our method can be summarized into two procedures. First,
cohensive groups are detected with modularity-based graph convolutional
networks. Then the suspiciousness of each group is measured by several anomaly
indicators from both individual and group levels. The fake reviewer groups can
be finally detected through suspiciousness. Extensive experiments are conducted
on real-world datasets, and the results show that our proposed method is
effective in detecting fake reviewer groups compared with the state-of-the-art
baselines.
