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.

Framing analysis is predominantly qualitative and quantitative, examining a small dataset with manual coding. Easy access to digital data in the last two decades prompts scholars in both computation and social sciences to utilize various computational methods to explore frames in large-scale datasets. The growing scholarship, however, lacks a comprehensive understanding and resources of computational framing analysis methods. Aiming to address the gap, this article surveys existing computational framing analysis approaches and puts them together. The research is expected to help scholars and journalists gain a deeper understanding of how frames are being explored computationally, better equip them to analyze frames in large-scale datasets, and, finally, work on advancing methodological approaches.