Interpretable Short Video Rumor Detection Based on Modality Tampering

Abstract

With the rapid development of social media and short video applications in recent years, browsing short videos has become the norm. Due to its large user base and unique appeal, spreading rumors via short videos has become a severe social problem. Many methods simply fuse multimodal features for rumor detection, which lack interpretability. For short video rumors, rumor makers create rumors by modifying and/or splicing different modal information, so we should consider how to detect rumors from the perspective of modality tampering. Inspired by cross-modal contrastive learning, we propose a novel short video rumor detection framework by designing two pretraining tasks: modality tampering detection and inter-modal matching, imbuing the model with the ability to detect modality tampering and employing it for downstream rumor detection tasks. In addition, we design an interpretability mechanism to make the rumor detection results more reasonable by backtracking the model’s decision-making process. The experimental results show that the method on the short video rumor dataset has an improvement of about 4.6%-12% in macro-F1 compared with other models and can explain whether the short video is a rumor or not through the perspective of modality tampering.