Multimodal Fake News Detection Technology Based on Deep Learning

With the development of science and technology, incidents of fake news triggering social crises occur from time to time. The change of media media makes false news gradually change from text form to multi-modal form with graphic and text coexisting. Multimodal fake news carries heterogeneous forms of text and image information, and research on the multimodal content of fake news can improve the effect of fake news detection. Existing multimodal fake news detection methods have the following deficiencies. First of all, in terms of feature extraction, most methods do not consider their own effective features, and features are not universal at this stage. Secondly, in terms of feature fusion, in the previous fake news detection methods that used image features combined with text features, the feature information in the news was simply spliced, without considering the interactive information between the two modalities. Finally, in terms of model structure, few models can verify the effectiveness of extracted features. According to the problems encountered in the current research, this paper conducts multi-modal fake news detection research based on deep learning, which further improves the accuracy of false news detection.