提出新框架，通过放大跨模态矛盾提升假新闻检测能力
Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection
- 将事实与情感分离，识别真实矛盾而非忽略差异
- 通过迭代极化增强关键冲突特征，准确率提升3.52%
- 适合关注多模态内容安全、对抗性伪造的科研与应用者
当前多模态假新闻检测依赖一致性融合，但该方法将关键跨模态差异误判为噪声，导致过度平滑，削弱了伪造证据。主流一致性融合机制会最小化特征差异以对齐模态，却因无意中平滑了伪造的核心线索而失效。为此，我们提出动态冲突-共识框架（DCCF），一种主动寻找不一致的新型范式，旨在放大而非抑制矛盾。首先，DCCF将输入解耦至独立的事实与情感空间，区分客观不一致与情绪抵触。其次，采用受物理启发的特征动力学，迭代极化表示，主动提取最具信息量的冲突。最后，通过冲突-共识机制，将局部差异与全局上下文对比，实现稳健判断。在三个真实数据集上的广泛实验表明，DCCF持续优于现有先进方法，平均准确率提升3.52%。
原文摘要 · Abstract (English)
Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal discrepancies as noise, leading to over-smoothing, which dilutes critical evidence of fabrication. Mainstream consistency-based fusion inherently minimizes feature discrepancies to align modalities, yet this approach fundamentally fails because it inadvertently smoothes out the subtle cross-modal contradictions that serve as the primary evidence of fabrication. To address this, we propose the Dynamic Conflict-Consensus Framework (DCCF), an inconsistency-seeking paradigm designed to amplify rather than suppress contradictions. First, DCCF decouples inputs into independent Fact and Sentiment spaces to distinguish objective mismatches from emotional dissonance. Second, we employ physics-inspired feature dynamics to iteratively polarize these representations, actively extracting maximally informative conflicts. Finally, a conflict-consensus mechanism standardizes these local discrepancies against the global context for robust deliberative judgment.Extensive experiments conducted on three real world datasets demonstrate that DCCF consistently outperforms state-of-the-art baselines, achieving an average accuracy improvement of 3.52\%.
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