When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms (2026.acl-long)
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| Challenge: | Existing fact-checking pipelines focus on written claims, not on audio . authors argue that audio misinformation is structurally different because it is both spoken and conversational . |
| Approach: | They argue that audio misinformation is structurally different because it is both spoken and conversational . they argue that advancing fact-checking requires rethinking verification pipelines around spoken and conversations . |
| Outcome: | The proposed method fails on audio because it is both spoken and conversational . podcasts exceed 4.3 million distinct shows, reaching an estimated 500 million listeners globally . |
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Mubashara Akhtar, Michael Schlichtkrull, Zhijiang Guo, Oana Cocarascu, Elena Simperl, Andreas Vlachos
| Challenge: | Existing studies on automated fact-checking focus on text, but they focus on a single modality, text . multimodal misinformation is perceived as more credible by humans and spreads faster than text-only counterparts. |
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| Challenge: | Automated fact-checking is often presented as an epistemic tool fact-seekers, social media consumers, and other stakeholders can use to fight misinformation. |
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| Challenge: | social media has made it easy for everyone to share and spread information online. |
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| Challenge: | Using psycholinguistic features to distinguish lies from true statements is a difficult task and a problem to be solved. |
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| Challenge: | Existing fact-checking models trained on non-dialogue data fail to perform well on this task. |
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Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023.emnlp-main)
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| Challenge: | Prior work has shown that typical fact-checking models struggle with claims made in conversation. |
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Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
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Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li
| Challenge: | Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media . |
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Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
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| Challenge: | fabricated stories and hoaxes are still pervading our cyberspace. |
| Approach: | They propose a framework to search for fact-checking articles that address the content of an original tweet that may contain misinformation posted by online users. |
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Detection and Resolution of Rumors and Misinformation with NLP (2020.coling-tutorials)
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| Challenge: | Detecting false and misleading claims on the web is a sub-field of NLP . this half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
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Automated Fact Checking: Task Formulations, Methods and Future Directions (C18-1)
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| Challenge: | Recent research on fact checking has focused on misinformation . however, relevant papers and articles have been published in research communities that are unaware of each other and use inconsistent terminology. |
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