A cross-view contrastive learning-based spammer group detection algorithm for heterogeneous networks

Abstract Malicious sellers frequently collaborate with spammers to fabricate reviews for promoting their products. These spammers act strategically in groups and have even formed black-and-gray industry chains. Researchers have proposed Frequent Item Mining-based, review burst-based, and graph-based schemes for spammer group detection. However, existing graph-based schemes often model reviewer relationships as a homogeneous network, failing to fully utilize the relationship semantics between reviewers or account for the burst characteristics of reviews, resulting in poor detection performance. Thus, this research proposes a cross-view contrastive learning-based spammer group detection algorithm for heterogeneous networks. To mine and embed the burst characteristics of reviews, we first filter out the target products and mine the active sessions of reviews about these products. We then construct a heterogeneous network featuring four node types and devise three unique meta-paths. Additionally, we utilize the cross-view contrastive learning method to learn the reviewer embeddings and apply DBSCAN to identify suspected groups for subsequent cleansing and ranking, ultimately determining potential spammer groups. Our experiments show the detection performance of the proposed scheme outperforms baseline ones.