构建多粒度假新闻数据集，揭示每条假新闻的独特造假模式。
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
- 提出多粒度标注框架，细粒度识别假新闻的伪造特征。
- 新数据集在多模态检测任务上显著提升难度，推动研究深入。
- 适合关注假新闻溯源与多模态分析的研究者使用。
社交平台在提供信息便利的同时，也充斥着大量虚假新闻，造成负面影响。自动化的多模态假新闻检测具有重要意义。现有数据集仅提供真假二分类标签，但真实新闻相似，而每条假新闻各有不同。这些数据集无法反映多模态假新闻的复杂混合特性。为此，我们构建了多粒度属性标注的假新闻检测数据集 \\(amg\\)，揭示其内在伪造模式。同时提出多粒度线索对齐模型 \\our，实现多模态假新闻检测与归因。实验表明，\\amg 是一个具有挑战性的数据集，其归因设定为未来研究开辟了新方向。
原文摘要 · Abstract (English)
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset \amg, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model \our to achieve multimodal fake news detection and attribution. Experimental results demonstrate that \amg is a challenging dataset, and its attribution setting opens up new avenues for future research.
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