Contrastive learning of cross-modal information enhancement for multimodal fake news detection

With the rapid development of the Internet, the existence of fake news and its rapid spread has brought many negative effects to the society. Consequently, the fake news detection task has become increasingly important over the past few years. Existing methods are predominantly unimodal methods or the multimodal representation of unimodal fusion for fake news detection. However, the large number of model parameters and the interference of noisy data increase the risk of overfitting. Thus, we construct an information enhancement and contrast learning framework by introducing Improved Low-rank Multimodal Fusion approach for Fake News Detection (ILMF-FND), which aims to reduce the noise interference and achieve efficient fusion of multimodal feature vectors with fewer parameters. In detail, an encoder extracts the feature vectors of text and images, which are subsequently refined using the Multi-gate Mixture-of-Experts. The refined features are mapped into the same space for semanteme sharing. Then, a cross-modal fusion is performed, resulting in that an efficient and highly precision fusion of text and image features is done with fewer parameters. Besides, we design an adaptive mechanism that can adjust the weights of the final components according to the modal fitness before inputting them into the classifier to achieve the best detection results in the current state. We evaluate the performance of ILMF-FND and the competitive baselines on two public datasets, i.e., Twitter and Weibo. The results indicate that our ILMF-FND greatly minimizes the number of parameters while outperforming the best baseline in terms of accuracy by 0.2% and 1.1% on the Weibo and Twitter datasets, respectively.