Detecting fake reviewers from the social context with a graph neural network method

With the development of mobile Web technologies, people can easily seek advice from social media before making purchases or decisions. Some companies employ expert writers to fabricate reviews or use automated techniques to improve the appeal of their products or services, or to undermine the credibility of their rivals. This obstructs the detection of fake reviews and reviewers. This paper proposes a novel graph neural network-based framework for detecting spammers, who originate fake reviews in discussion forums to capture information from different social network combinations in various subgraphs. These subgraphs include a complete social context graph, homogeneous user-user subgraph, and heterogeneous user-post subgraph. A novel two-stage architecture with focal loss was designed to create a training model. This model can be applied to solve the issue of imbalance data classification. The proposed framework was applied to evaluate a ground truth dataset collected from an actual fraudulent review event on a discussion forum. The experimental results show that this aggregate social context representation method can be effectively applied to detect fake reviewers.