Everything We Hear: Towards Tackling Misinformation in Podcasts
Not on the map. Why?
Abstract (arXiv)
Advances in generative AI, the proliferation of large multimodal models (LMMs), and democratized open access to these technologies have direct implications for the production and diffusion of misinformation. In this prequel, we address tackling misinformation in the unique and increasingly popular context of podcasts. The rise of podcasts as a popular medium for disseminating information across diverse topics necessitates a proactive strategy to combat the spread of misinformation. Inspired by the proven effectiveness of textitauditory alerts in contexts like collision alerts for drivers and error pings in mobile phones, our work envisions the application of auditory alerts as an effective tool to tackle misinformation in podcasts. We propose the integration of suitable auditory alerts to notify listeners of potential misinformation within the podcasts they are listening to, in real-time and without hampering listening experiences. We identify several opportunities and challenges in this path and aim to provoke novel conversations around instruments, methods, and measures to tackle misinformation in podcasts.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcastscited by 4
- 2026When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platformscited by 1
- 2025Listening Between the Lines: Decoding Podcast Narratives with Language Modelingcited by 0
- 2026From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topicscited by 0
- 2026TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audiocited by 0
- 2026TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio3 Sep 2026
- 2026When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms18 Apr 2026
- 2026From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topics16 Jan 2026
- 2025Listening Between the Lines: Decoding Podcast Narratives with Language Modeling7 Nov 2025
- 2025Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts22 Jan 2025
Similar, not cited
Close in wording, no citation either way
- 2025How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
- 2026To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech
- 2026Beyond Transcripts: A Renewed Perspective on Audio Chaptering
- 2026AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
- 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark
- 2025Mellow: a small audio language model for reasoning
- 2025Not in Sync: Unveiling Temporal Bias in Audio Chat Models
- 2026Multi-Source Evidence Fusion for Audio Question Answering