DARE-Net: Domain-Adaptive rationale evolution network for cross-domain false advertising detection in live-streaming e-commerce

Abstract Consumer reviews provide valuable post-hoc evidence for detecting false advertising in live-streaming e-commerce. However, detecting unseen or emerging deceptive promotions remains challenging due to noisy reviews, incomplete context, and cross-category variation. To address these challenges, we propose DARE-Net, a Domain-Adaptive Rationale Evolution Network for cross-domain false advertising detection from consumer reviews. Instead of directly transferring general semantic or topical patterns, DARE-Net formulates the task as rationale-centric discrepancy evidence learning. Specifically, a hierarchical discrepancy-aware rationale mining module extracts multi-granularity deception-related rationales from noisy reviews; a domain-adaptive rationale transformation module disentangles domain-specific bias from transferable deception evidence; and an evidence-boundary decision module adaptively aggregates decisive rationales with class-adaptive margins to alleviate imbalance-induced boundary bias. Experiments on multi-domain live-streaming e-commerce review datasets show that DARE-Net consistently outperforms state-of-the-art cross-domain and task-related detection methods, achieving a relative improvement of 2.52% over the best-performing baseline. Further analysis demonstrates that DARE-Net can capture transferable deception evidence and provide interpretable predictions across diverse product domains, indicating its effectiveness for robust false advertising detection in dynamic live-streaming e-commerce environments.