Multimodal Fake News Detection via CLIP-Guided Learning

Fake news detection (FND) has attracted much research interests in social forensics. Many existing approaches introduce tailored attention mechanisms to fuse unimodal features. However, they ignore the impact of cross-modal similarity between modalities. Meanwhile, the potential of pretrained multimodal feature learning models in FND has not been well exploited. This paper proposes an FND-CLIP framework, i.e., a multimodal Fake News Detection network based on Contrastive Language-Image Pretraining (CLIP). FND-CLIP extracts the deep representations together from news using two unimodal encoders and two pair-wise CLIP encoders. The CLIP-generated multimodal features are weighted by CLIP similarity of the two modalities. We also introduce a modality-wise attention module to aggregate the features. Extensive experiments are conducted and the results indicate that the proposed framework has a better capability in mining crucial features for fake news detection. The proposed FND-CLIP can achieve better performances than previous works on three typical fake news datasets.