Chun, C., Zhang, D.C. and Lee, D. (2026) When misinformation speaks and converses: rethinking fact-checking in audio platforms. In: Liakata, M., Moreira, V.P., Zhang, J. and Jurgens, D., (eds.) Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 02-07 Jul 2026, San Diego, California, United States. Vol. 1. Association for Computational Linguistics, pp. 2060-2075. ISBN: 9798891763906. ISSN: 0736-587X. EISSN: 9061-6464.
Abstract
Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipelines are mostly designed for written claims, overlooking the unique properties of spoken media. We argue that audio misinformation is not merely textual content with transcripts: it is structurally different because it is both spoken—carrying persuasive force through prosody, pacing, and emotion—and conversational—unfolding across turns, speakers, and episodes. These dual properties introduce verification difficulties that traditional methods rarely face. This position paper synthesizes evidence across modalities and platforms, examines datasets and methods, and highlights why existing pipelines fail on audio. We argue that advancing fact-checking requires rethinking verification pipelines around the spoken and conversational realities of audio.
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Editors: |
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| Copyright, Publisher and Additional Information: | © 2026 Association for Computational Linguistics (ACL). Licensed on a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
| Date Deposited: | 11 Nov 2026 15:50 |
| Last Modified: | 11 Nov 2026 15:50 |
| Status: | Published |
| Publisher: | Association for Computational Linguistics |
| Refereed: | Yes |
| Identification Number: | 10.18653/v1/2026.acl-long.93 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:247730 |
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Filename: 2026.acl-long.93.pdf
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