Fake review detection with label-consistent and hierarchical-relation-aware graph contrastive learning

• Published in: Knowledge-based systems Vol. 302; p. 112385
• Main Authors: Yao, Jianrong, Jiang, Ling, Shi, Chenglong, Yan, Surong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 25.10.2024
• Subjects: Fake review detection; Graph contrastive learning; Graph neural networks; Graph contrastive learning; Graph neural networks; Fake review detection
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2024.112385

The rapid increase in fake reviews in e-commerce presents a considerable challenge, as it misleads consumers and compromises market integrity. Recently, graph-based models for detecting fake reviews have emerged as promising solutions. However, conventional methods and basic graph neural network techniques often fail to recognize the deceptive tactics of fraudsters, negatively affecting their efficacy. In response, we propose the label-consistent and hierarchical-relation-aware graph contrastive learning (LRGCL) framework, which is specifically designed to identify fake reviews, even when fraudsters employ various disguises. This framework implements a label-consistent graph contrastive learning approach to distinctly identify positive and negative samples, ensuring clear separation between authentic and fraudulent nodes despite feature camouflage. Additionally, LRGCL classifies negative samples into several categories using a hierarchical approach that leverages relationships within a multi-relation graph, thereby counteracting relation camouflage. The integration of these two strategies significantly reduces the effectiveness of these camouflages, leading to more precise detection of fake reviews. Empirical tests using real-world datasets validate the superior efficacy of LRGCL, as it surpasses current leading methods across key performance metrics.