Temporal Knowledge Consistency for Spammer Groups Detection via Contrastive Learning

Online reviews on platforms such as Amazon and Yelp significantly influence consumer decisions and business reputations. However, spammers form groups that strategically control product reviews over specific periods to manipulate consumer sentiment and decision-making. Traditional spammer group detection methods face two primary issues: 1) knowledge marginalization: interactions dominate the model to form candidate groups, potentially marginalizing valuable structured knowledge; and 2) temporal knowledge discrepancy: inconsistencies in users’ temporal activities and behavioral features lead to blurry classification of candidate groups. To address these two limitations, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal knowledge consistency for spammer groups detection via contrastive learning</i> called TKCCL. We employ dual-view encoders derived from knowledge graphs and heterogeneous information networks to learn informative representations, thereby alleviating knowledge marginalization. TKCCL maps the temporal knowledge as vectors to measure consistency, enhancing users’ rating proximity and temporal synchronization in dual views, thereby reducing temporal knowledge discrepancies. We optimize spammer group detection by modeling it as a greedy set cover problem, which enhances the method’s responsiveness to dynamic spam strategies. Experimental results on four public datasets demonstrate that TKCCL substantially outperforms existing methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NeenLee/TKCCL</uri>.