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.

翻译：随着短视频平台成为新闻传播的重要渠道之一，我国各大短视频平台已逐渐成为虚假新闻的新滋生地。然而，由于短视频蕴含海量信息与特征，且视频间特征存在严重同质化与相似性，短视频谣言的识别并不容易。为减缓短视频谣言的传播，本课题组在权衡各算法优缺点后，决定通过构建多模态特征融合并引入外部知识的方式对短视频谣言进行检测。检测思路如下：（1）数据集构建：建立包含多特征的短视频数据集；（2）多模态谣言检测模型：首先采用TSN（时序分段网络）视频编码模型提取视频特征；其次融合OCR（光学字符识别）与ASR（自动语音识别）提取文本信息，再利用BERT模型实现文本特征与视频特征的融合；（3）最后利用对比学习实现区分：先爬取外部知识，再借助向量数据库完成外部知识的引入与最终分类输出结构。本项研究始终以实际需求为导向，相关研究成果将在短视频谣言识别、社会舆论管控等众多实际场景中发挥重要作用。