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In the digital era, social media has become a major conduit for informationdissemination, yet it also facilitates the rapid spread of misinformation.Traditional misinformation detection methods primarily focus on surface-levelfeatures, overlooking the crucial roles of human empathy in the propagationprocess. To address this gap, we propose the Dual-Aspect Empathy Framework(DAE), which integrates cognitive and emotional empathy to analyzemisinformation from both the creator and reader perspectives. By examiningcreators' cognitive strategies and emotional appeals, as well as simulatingreaders' cognitive judgments and emotional responses using Large LanguageModels (LLMs), DAE offers a more comprehensive and human-centric approach tomisinformation detection. Moreover, we further introduce an empathy-awarefiltering mechanism to enhance response authenticity and diversity.Experimental results on benchmark datasets demonstrate that DAE outperformsexisting methods, providing a novel paradigm for multimodal misinformationdetection.

Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection