RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection

• URL: http://arxiv.org/abs/2412.18254v1
• Date: Tue, 24 Dec 2024 08:08:29 GMT
• Title: RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection
• Authors: Xinquan Yu, Ziqi Sheng, Wei Lu, Xiangyang Luo, Jiantao Zhou,
• Abstract summary: Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. We present a residual-aware compensation network with multi-granularity constraints for fake news detection, that aims to sufficiently interact and fuse cross-modal features. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method.
• Score: 24.91068046428761
• License:
• Abstract: Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method.

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