In recent years, fake news detection has been a significant task attracting much attention. However, most current approaches utilize the features from a single modality, such as text or image, while the comprehensive fusion between features of different modalities has been ignored. To deal with the above problem, we propose a novel model named Bidirectional Cross-Modal Fusion (BCMF), which comprehensively integrates the textual and visual representations in a bidirectional manner. Specifically, the proposed model is decomposed into four submodules, i.e., the input embedding, the image2text fusion, the text2image fusion, and the prediction module. We conduct intensive experiments on four real-world datasets, i.e., Weibo, Twitter, Politi, and Gossip. The results show 2.2, 2.5, 4.9, and 3.1 percentage points of improvements in classification accuracy compared to the state-of-the-art methods on Weibo, Twitter, Politi, and Gossip, respectively. The experimental results suggest that the proposed model could better capture integrated information of different modalities and has high generalizability among different datasets. Further experiments suggest that the bidirectional fusions, the number of multi-attention heads, and the aggregating function could impact the performance of the cross-modal fake news detection. The research sheds light on the role of bidirectional cross-modal fusion in leveraging multi-modal information to improve the effect of fake news detection.

近年来，假新闻检测一直是一项备受关注的重大任务。然而，当前大多数方法都利用单一模态的特征，例如文本或图像，而忽略了不同模态特征之间的综合融合。为了解决上述问题，我们提出了一种名为双向跨模态融合（BCMF）的新模型，它以双向方式全面集成了文本和视觉表示。具体来说，所提出的模型被分解为四个子模块，即输入嵌入、图像2文本融合、文本2图像融合和预测模块。我们在四个真实世界的数据集上进行了密集的实验，即微博、Twitter、Politi 和 Gossip。结果显示 2.2、2.5、4.9 和 3。与微博、Twitter、Politi 和 Gossip 上的最先进方法相比，分类准确率分别提高了 1 个百分点。实验结果表明，该模型能够更好地捕捉不同模态的综合信息，并且在不同数据集之间具有较高的泛化性。进一步的实验表明，双向融合、多注意力头的数量和聚合函数可能会影响跨模态假新闻检测的性能。该研究阐明了双向跨模态融合在利用多模态信息提高假新闻检测效果中的作用。实验结果表明，该模型能够更好地捕捉不同模态的综合信息，并且在不同数据集之间具有较高的泛化性。进一步的实验表明，双向融合、多注意力头的数量和聚合函数可能会影响跨模态假新闻检测的性能。该研究阐明了双向跨模态融合在利用多模态信息提高假新闻检测效果中的作用。实验结果表明，该模型能够更好地捕捉不同模态的综合信息，并且在不同数据集之间具有较高的泛化性。进一步的实验表明，双向融合、多注意力头的数量和聚合函数可能会影响跨模态假新闻检测的性能。该研究阐明了双向跨模态融合在利用多模态信息提高假新闻检测效果中的作用。