Multimodal Short Video Rumor Detection System Based on Contrastive Learning

Abstract

With short video platforms becoming one of the important channels for news sharing, major short video platforms in China have gradually become new breeding grounds for fake news. However, it is not easy to distinguish short video rumors due to the great amount of information and features contained in short videos, as well as the serious homogenization and similarity of features among videos. In order to mitigate the spread of short video rumors, our group decides to detect short video rumors by constructing multimodal feature fusion and introducing external knowledge after considering the advantages and disadvantages of each algorithm. The ideas of detection are as follows: (1) dataset creation: to build a short video dataset with multiple features; (2) multimodal rumor detection model: firstly, we use TSN (Temporal Segment Networks) video coding model to extract video features; then, we use OCR (Optical Character Recognition) and ASR (Automatic Character Recognition) to extract video features. Recognition) and ASR (Automatic Speech Recognition) fusion to extract text, and then use the BERT model to fuse text features with video features (3) Finally, use contrast learning to achieve distinction: first crawl external knowledge, then use the vector database to achieve the introduction of external knowledge and the final structure of the classification output. Our research process is always oriented to practical needs, and the related knowledge results will play an important role in many practical scenarios such as short video rumor identification and social opinion control