从认知与情感双角度建模用户共情，提升谣言检测效果
Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection
- 融合创作者认知策略与情感诉求，模拟读者心理反应
- 在多个基准数据集上超越现有方法，显著提升检测准确率
- 适合关注社交传播机制与人机共情的AI研究者
数字时代，社交媒体成为信息传播主渠道，但也加速了虚假信息的扩散。传统检测方法仅关注表面特征，忽视人类共情在传播中的关键作用。为此，我们提出双面共情框架（DAE），从创作者与读者双视角整合认知与情感共情，通过大语言模型模拟读者的认知判断与情感反应，实现更全面、以人为本的多模态虚假信息检测。此外，引入共情感知过滤机制，增强响应的真实性和多样性。在多个基准数据集上的实验表明，DAE显著优于现有方法，为多模态虚假信息检测提供了新范式。
原文摘要 · Abstract (English)
In the digital era, social media has become a major conduit for information dissemination, yet it also facilitates the rapid spread of misinformation. Traditional misinformation detection methods primarily focus on surface-level features, overlooking the crucial roles of human empathy in the propagation process. To address this gap, we propose the Dual-Aspect Empathy Framework (DAE), which integrates cognitive and emotional empathy to analyze misinformation from both the creator and reader perspectives. By examining creators' cognitive strategies and emotional appeals, as well as simulating readers' cognitive judgments and emotional responses using Large Language Models (LLMs), DAE offers a more comprehensive and human-centric approach to misinformation detection. Moreover, we further introduce an empathy-aware filtering mechanism to enhance response authenticity and diversity. Experimental results on benchmark datasets demonstrate that DAE outperforms existing methods, providing a novel paradigm for multimodal misinformation detection.
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