通过语义增强与情绪推理提升假新闻识别准确率
SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
- 用大模型生成图像摘要增强语义理解
- 结合负向情绪特征，检测结果优于现有方法
- 适合关注多模态假新闻检测的研究者
以往多模态假新闻检测研究主要关注跨模态特征对齐与图文一致性，却忽视了大模型的语义增强作用及新闻的情绪特征。事实上，虚假新闻往往包含更多负向情绪。为此，我们提出一种新型语义增强与情绪推理网络（SEER）。通过大模型生成图像摘要以增强语义理解，并设计专家级情绪推理模块，模拟真实场景优化情绪特征，推断新闻真实性。在两个真实数据集上的大量实验表明，SEER在多模态假新闻检测上优于当前最优基线方法。
原文摘要 · Abstract (English)
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. However, they overlook the semantic enhancement effects of large multimodal models and pay little attention to the emotional features of news. In addition, people find that fake news is more inclined to contain negative emotions than real ones. Therefore, we propose a novel Semantic Enhancement and Emotional Reasoning (SEER) Network for multimodal fake news detection. We generate summarized captions for image semantic understanding and utilize the products of large multimodal models for semantic enhancement. Inspired by the perceived relationship between news authenticity and emotional tendencies, we propose an expert emotional reasoning module that simulates real-life scenarios to optimize emotional features and infer the authenticity of news. Extensive experiments on two real-world datasets demonstrate the superiority of our SEER over state-of-the-art baselines.
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