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

Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines