  Automatic detection of multimodal fake news has gained a widespread attention
recently. Many existing approaches seek to fuse unimodal features to produce
multimodal news representations. However, the potential of powerful cross-modal
contrastive learning methods for fake news detection has not been well
exploited. Besides, how to aggregate features from different modalities to
boost the performance of the decision-making process is still an open question.
To address that, we propose COOLANT, a cross-modal contrastive learning
framework for multimodal fake news detection, aiming to achieve more accurate
image-text alignment. To further improve the alignment precision, we leverage
an auxiliary task to soften the loss term of negative samples during the
contrast process. A cross-modal fusion module is developed to learn the
cross-modality correlations. An attention mechanism with an attention guidance
module is implemented to help effectively and interpretably aggregate the
aligned unimodal representations and the cross-modality correlations. Finally,
we evaluate the COOLANT and conduct a comparative study on two widely used
datasets, Twitter and Weibo. The experimental results demonstrate that our
COOLANT outperforms previous approaches by a large margin and achieves new
state-of-the-art results on the two datasets.
