Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection
This addresses misinformation spread on social media by offering a more human-centric approach, though it appears incremental as it builds on existing multimodal detection methods.
The paper tackles misinformation detection by integrating cognitive and emotional empathy into a Dual-Aspect Empathy Framework (DAE), which outperforms existing methods on benchmark datasets.
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.