A two-branch multimodal fake news detection model based on multimodal bilinear pooling and attention mechanism

Introduction Fake news spread in various areas has a major negative impact on social life. Meanwhile, fake news with text and visual content is more compelling than text-only content and quickly spreads across social media. Therefore, detecting fake news is a pressing task for the current society. Methods Concern the problem of extracting insufficient features, and the inability to merge multi-modality features effectively in detecting fake news. In this article, we propose a method for detecting fake news by fusing text and visual data. Firstly, we use two-branch to learn hidden layer information of modality to obtain more helpful features. Then we proposed a multimodal bilinear pooling mechanism to better merge textual and visual features and an attention mechanism to capture multimodal internal relationships for the detection of fake news. Results and discussion The experimental results demonstrated that our methodology outperformed the current state-of-the-art methodology on publicly accessible Weibo and Twitter datasets.