A Cross-Platform Transfer Learning Framework for Detecting Coordinated Opinion Manipulation Across E-Commerce Ecosystems
Keywords:
coordinated opinion manipulation, transfer learning, domain adaptation, fake review detection, e-commerce, graph neural networks, temporal behavioral analysisAbstract
The proliferation of coordinated opinion manipulation (COM) across major e-commerce ecosystems poses an escalating threat to consumer trust and platform integrity. Existing detection approaches are predominantly platform-specific and fail to generalize across heterogeneous review environments such as Amazon, Yelp, and JD.com. This paper proposes a Cross-Platform Transfer Learning (CPTL) framework that leverages domain-adversarial neural networks combined with a graph-based behavioral encoder to detect COM campaigns regardless of platform origin. By jointly modeling linguistic patterns, reviewer interaction graphs, and temporal bursting signals — including entropy gap scores, youth scores, and singleton reviewer ratios — the proposed system achieves knowledge transfer from label-rich source platforms to data-scarce target platforms. Extensive experiments on three real-world e-commerce datasets demonstrate that the CPTL framework outperforms strong baselines, achieving F1 scores of 0.891, 0.874, and 0.863 on Amazon, Yelp, and JD.com respectively, with cross-platform transfer degradation consistently below 6%. Receiver operating characteristic (ROC) curve analysis confirms robust detection under both in-domain and transfer conditions, while analysis of singleton reviewer rating distributions reveals the concentrated five-star manipulation signature that anchors the framework's graph encoding pathway.
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