Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.

翻译：在本文中,我们研究了在大型多式联运数据集中进行多式联运假新闻探测的问题,我们建议建立一个多式网络结构,使不同层次和类型的信息融合成为可能。除了张贴的文字和视觉内容外,我们还进一步利用次级信息,即用户评论和元数据。我们在多个层面整合信息,以说明模式的具体内在结构。我们的结果显示,多式联运分析对于任务非常有效,所有模式在适当结合时都会做出积极贡献。