Detecting review spammer groups based on generative adversarial networks

The detection of spammer groups has recently gained more attention. However, the existing spammer group detection approaches rely on manual feature engineering to design spam indicators or extract features to capture the aggregated behavioral patterns exhibited by spammer groups. This is costly and time-consuming. To address this limitation, we formulate spammer group detection as a problem of finding distribution differences between normal user groups and spammer groups, and propose a review spammer group detection approach based on generative adversarial networks. First, we perform multiview sequence sampling on the dataset and utilize the Word2Vec model to obtain low-dimensional vector representations of users. Second, we construct the N-nearest neighbor user relationship graph according to the vector similarities in the embedding space. In the closed neighborhood of each node, we use the DBSCAN algorithm to find candidate groups. Finally, we construct a generative adversarial network model for spammer group detection, which uses the sum of generator reconstruction loss and discriminator loss to evaluate the spamicities of candidate groups. The experimental results on three real-world review datasets indicate that the proposed approach outperforms the baseline approaches in detection performance.