Leveraging Supplementary Information for Multi-Modal Fake News Detection

When natural disasters such as floods, earthquakes, terrorist attacks, and industrial accidents occur, first responders, news agencies, and victims increasingly use social media platforms as primary communication channels for disseminating reliable situational information to the public. Although social media is a powerful tool for spreading news, it may also facilitate the spread of fake news, which poses a threat to societal security. Traditionally, verification methods require a great deal of human and social resources, and they are not able to keep pace with the rate at which news is disseminated. In order to determine the authenticity of news articles, we propose a multi-modal approach that analyzes different modalities of information. In this paper, we employ an image captioning model to generate textual descriptions of news images, which provides supplementary information for verification. Our experimental evaluations on the real-world dataset have demonstrated that the proposed method achieves higher performance than baseline methods.