  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.
