Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video's contents. After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines. Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators' background and the content of the videos.

翻译：极化现象和印象市场共同使用户在网络信息导航中面临困难。尽管检测虚假或误导性文本的研究已取得显著进展，但多模态数据集受到的关注相对较少。为补充现有资源，我们提出多模态视频误导性标题（VMH）数据集，其中包含视频内容及标注者关于标题是否准确反映视频内容的判断。在完成数据收集与标注后，我们分析了用于检测误导性标题的多模态基线方法。我们的标注过程还重点关注标注者为何认为视频具有误导性，从而更深入理解标注者背景与视频内容之间的相互作用。