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.

翻译：社交媒体平台在促进信息获取的同时，也充斥着大量假新闻，带来了诸多负面影响。自动化的多模态假新闻检测是一项值得深入探索的研究方向。现有的多模态假新闻数据集仅提供真实或虚假的二元标签。然而，真实的新闻往往相似，而每条假新闻却各有其独特的虚假方式。这些数据集未能反映各类多模态假新闻混杂的本质。为弥补这一不足，我们构建了一个属性多粒度多模态假新闻检测数据集 \amg，以揭示其内在的虚假模式。此外，我们提出了一种多粒度线索对齐模型 \our，以实现多模态假新闻的检测与属性归因。实验结果表明，\amg 是一个具有挑战性的数据集，其属性归因设定为未来研究开辟了新的途径。