The increasing popularity of social media facilitates the propagation of fake news, posing a major threat to the government and journalism, and thereby making how to detect fake news from social media an urgent requirement. In general, multimodal-based methods can achieve better performance because of the complementation among different modalities. However, the majority of them simply concatenate features from different modalities, failing to well preserve the mutual information in common features. To address this issue, a novel framework named semantic-enhanced multimodal fusion network is proposed for fake news detection, which can better capture mutual features among events and thus benefit the detection of fake news. This model consists of three subnetworks, namely multimodal fusion and event domain adaptation networks as well as the fake news detector. Specifically, the multimodal fusion network aims to extract deep features from texts and images and fuse them into a common semantic feature known as a snapshot. Then, the fake news detector can learn the representation of posts. Finally, the event domain adaptation network can single out and remove the peculiar features of each event, and keep shared features among events. The experimental results show that the proposed model outperforms some state-of-the-art approaches on two real-world multimedia data sets.

中文翻译：

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用于假新闻检测的语义增强多模态融合网络

社交媒体的日益普及促进了假新闻的传播，对政府和新闻业构成了重大威胁，从而使如何从社交媒体中检测假新闻成为迫切要求。通常，由于不同模态之间的互补，基于多模态的方法可以获得更好的性能。然而，它们中的大多数只是简单地连接来自不同模态的特征，未能很好地保留共同特征中的相互信息。为了解决这个问题，提出了一种名为语义增强多模式融合网络的新框架用于假新闻检测，它可以更好地捕获事件之间的相互特征，从而有利于假新闻的检测。该模型由三个子网组成，即多模态融合和事件域适应网络以及假新闻检测器。具体来说，多模态融合网络旨在从文本和图像中提取深层特征，并将它们融合成一个共同的语义特征，称为快照。然后，假新闻检测器可以学习帖子的表示。最后，事件域适应网络可以挑出并去除每个事件的特有特征，并保留事件之间的共享特征。实验结果表明，所提出的模型在两个真实世界的多媒体数据集上优于一些最先进的方法。最后，事件域适应网络可以挑出并去除每个事件的特有特征，并保留事件之间的共享特征。实验结果表明，所提出的模型在两个真实世界的多媒体数据集上优于一些最先进的方法。最后，事件域适应网络可以挑出并去除每个事件的特有特征，并保留事件之间的共享特征。实验结果表明，所提出的模型在两个真实世界的多媒体数据集上优于一些最先进的方法。