  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.
