Multi-Modal Fake News Detection via Bridging the Gap between Modals

Abstract

Multi-modal fake news detection aims to identify fake information through text and corresponding images. The current methods purely combine images and text scenarios by a vanilla attention module but there exists a semantic gap between different scenarios. To address this issue, we introduce an image caption-based method to enhance the model’s ability to capture semantic information from images. Formally, we integrate image description information into the text to bridge the semantic gap between text and images. Moreover, to optimize image utilization and enhance the semantic interaction between images and text, we combine global and object features from the images for the final representation. Finally, we leverage a transformer to fuse the above multi-modal content. We carried out extensive experiments on two publicly available datasets, and the results show that our proposed method significantly improves performance compared to other existing methods.

Linguistic Features and Bi-LSTM for Identification of Fake News

“…An image caption-based strategy was proposed to improve the model's capacity to extract semantic information from pictures [19]. To bridge the semantic gap between language and ideas, authors first add picture description information into the text.…”