论文标题
FAKESV：一个具有丰富社会环境的多模式基准，可在短视频平台上进行虚假新闻检测
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms
论文作者
论文摘要
简短的视频平台已成为新闻共享的重要渠道，也是假新闻的新繁殖场。为了减轻这个问题，《假新闻》视频检测的研究最近受到了很多关注。现有作品面临两个障碍：综合和大规模数据集的稀缺性以及多模式信息的利用不足。因此，在本文中，我们构建了有关假新闻Fakesv的最大的中文简短视频数据集，其中包括新闻内容，用户评论和发布者。为了了解假新闻视频的特征，我们从不同的角度对FASEV进行了探索性分析。此外，我们提供了一个名为SV频率的新的多模式检测模型，该模型利用了交叉模式相关性来选择最有用的功能，并利用社交环境信息进行检测。广泛的实验评估了所提出的方法的优越性，并为未来的工作提供了不同方法和方式的详细比较。
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.
