When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms
Chaewan Chun, Delvin Ce Zhang, Dongwon Lee
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 47e5cd8d-1139-4ad3-bf25-c3c15f15cfb8Builds on25
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech RepresentationsAlexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael AuliNeurIPS 2020 · 9,451 citations
- Robust Speech Recognition via Large-Scale Weak SupervisionAlec Radford, Jong Wook Kim, Tao Xu, Greg Brockman et al.ICML 2023 · 6,966 citations
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionAkari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil et al.ICLR 2024 · 1,798 citations
- MedDialog: Large-scale Medical Dialogue DatasetsGuangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang et al.EMNLP 2020 · 163 citations
- Generating Fact Checking ExplanationsPepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle AugensteinACL 2020 · 130 citations
Related papers
- Social Debunking of Misinformation on WhatsApp: The Case for Strong and In-group TiesIrene V. Pasquetto, Eaman Jahani, Shubham Atreja, Matthew BaumCSCW 2022 · 59 citations
- VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-CheckingMark Rothermel, Marcus Kornmann, Marcus Rohrbach, Anna RohrbachACL 2026 · 4 citations
- Countering Misinformation via Emotional Response GenerationDaniel Russo, Shane P. Kaszefski-Yaschuk, Jacopo Staiano, Marco GueriniEMNLP 2023 · 4 citations
- Designing an Automated Fact-Checking Pipeline for Recent ClaimsArshia Arya, Gauthami Yenne, Deepak KumarCCS 2026
- Factoring Fact-Checks: Structured Information Extraction from Fact-Checking ArticlesShan Jiang, Simon Baumgartner, Abe Ittycheriah, Cong YuWWW 2020 · 28 citations
