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arXiv · 2211.10973

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

Peng Qi·Yuyan Bu·Juan Cao·Wei Ji·Ruihao Shui·Junbin Xiao·Danding Wang·Tat-Seng Chua
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Abstract

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.

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Peng Qi, Yuyan Bu, Juan Cao, Wei Ji, Ruihao Shui, Junbin Xiao, Danding Wang, Tat-Seng Chua. 2022-11-20. FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms. https://arxiv.org/abs/2211.10973

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