arXiv:2606.11420v1 Announce Type: new Abstract: Every day, millions absorb claims from podcasts and streams that no fact-checker ever sees. Spoken misinformation is built through conversation, where credibility comes not from facts alone but from how claims are framed, reinforced, or left unchallenged across turns. Yet fact-checking has focused on isolated text, leaving dialogue audio under-studied. We introduce MAD2, a new Multi-turn Audio Dialogues benchmark for spoken claim verification, containing 1,000 two-speaker dialogues with 3,368 check-worthy claims and approximately 10 hours of audi
The proliferation of audio-first content like podcasts and streams has created a new frontier for misinformation, which traditional text-based fact-checking methods cannot address.
This development represents a critical step towards tackling the pervasive issue of multimodal misinformation, which could otherwise undermine trust in information and disrupt societal discourse at scale.
The introduction of MAD2 explicitly addresses the need for context-aware, multimodal claim verification within spoken dialogues, enabling the development of AI tools for a previously unaddressed threat vector.
- · AI researchers
- · Social media platforms
- · Fact-checking organizations
- · Public discourse
- · Misinformation purveyors
AI models will be developed and refined to detect and flag misinformation in audio content.
Social platforms and content hosts will implement these AI tools, leading to automated content moderation in audio streams.
The development of these tools could lead to more regulated or curated audio content environments, potentially impacting freedom of speech or creating new forms of bias in content filtering.
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Read at arXiv cs.CL