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

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arxiv 2310.13859 v2 pith:EPUNFVZY submitted 2023-10-20 cs.CL cs.CV

Yoo Yeon Sung , Jordan Boyd-Graber , Naeemul Hassan This is my paper · ORCID
classification cs.CLcs.CV
keywords misleadingvideoannotatorsdatasetmultimodalheadlineheadlinesvideos
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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.

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  1. MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections

    cs.CL 2025-02 conditional novelty 7.0 of 10

    MODS uses per-document LLM speakers, a moderator with tailored queries, and a structured outline to write more comprehensive and balanced summaries of debatable queries.

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