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.

翻译：在数字时代，社交媒体已成为信息传播的主要渠道，但也助长了虚假信息的快速扩散。传统的虚假信息检测方法主要关注表层特征，忽视了人类共情在传播过程中的关键作用。为弥补这一不足，我们提出双维度共情框架（DAE），该框架整合认知共情与情感共情，从信息创建者与读者双重视角分析虚假信息。通过解析创建者的认知策略与情感诉求，并利用大语言模型（LLMs）模拟读者的认知判断与情感反应，DAE为虚假信息检测提供了更全面且以人为本的研究路径。此外，我们进一步引入共情感知过滤机制以提升回应的真实性与多样性。在基准数据集上的实验结果表明，DAE性能优于现有方法，为多模态虚假信息检测提供了新的范式。