Multi-modal Dual Collaborative Gather Transformer Network for Fake News Detection

Social media platforms are convenient platforms for people to share information,express opinions,and exchange ideas in their daily lives.With the increasing number of users,a large amount of data has emerged on social media websites.However,the authenticity of the shared information is difficult to be guaranteed due to users’ lack of verification.This situation has led to the widespread dissemination of a large amount of fake news on social media.However,existing methods suffer from the follo-wing limitations:1)Most existing methods rely on simple text and visual feature extraction,concatenating them to obtain multimodal features for detecting fake news,while ignoring the fine-grained intrinsic connections within and between modalities,and lacking retrieval and filtering of key information.2)There is a lack of guided feature extraction among multimodal information,with insufficient interaction and understanding between textual and visual features.To address these challenges,a novel multimodal dual-collaborative gather transformer network(MDCGTN) is proposed to overcome the limitations of existing methods.In the MDCGTN model,textual and visual features are extracted using a text-visual encoding network,and the obtained features are input into a multimodal gather transformer network for multimodal information fusion.The gathering mechanism is used to extract key information,fully capturing and fusing fine-grained relationships within and between modalities.In addition,a dual-collaborative mechanism is designed to integrate multimodal information in social media posts,enhancing interaction and understanding between modalities.Extensive experiments are conducted on two publicly available benchmark datasets.Compared to existing state-of-the-art benchmark methods,the proposed MDCGTN method achieves significant improvement in accuracy,demonstrating its superior performance in detecting fake news.