FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analysis of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and provide detailed comparisons of different methods and modalities for future works. Our dataset and codes are available in https://github.com/ICTMCG/FakeSV.

Authors

• Peng Qi Centre for Trusted Internet and Community, National University of Singapore
• Yuyan Bu Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Juan Cao Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Wei Ji School of Medicine, Yale University, New Haven, United States
• Ruihao Shui National University of Singapore
• Junbin Xiao National University of Singapore
• Danding Wang Institute of Computing Technology, Chinese Academy of Sciences
• Tat-Seng Chua National University of Singapore

Keywords

• General