  Fake news often involves multimedia information such as text and image to
mislead readers, proliferating and expanding its influence. Most existing fake
news detection methods apply the co-attention mechanism to fuse multimodal
features while ignoring the consistency of image and text in co-attention. In
this paper, we propose multimodal matching-aware co-attention networks with
mutual knowledge distillation for improving fake news detection. Specifically,
we design an image-text matching-aware co-attention mechanism which captures
the alignment of image and text for better multimodal fusion. The image-text
matching representation can be obtained via a vision-language pre-trained
model. Additionally, based on the designed image-text matching-aware
co-attention mechanism, we propose to build two co-attention networks
respectively centered on text and image for mutual knowledge distillation to
improve fake news detection. Extensive experiments on three benchmark datasets
demonstrate that our proposed model achieves state-of-the-art performance on
multimodal fake news detection.
