mlsanityData to 14 Oct 2026

Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

arXiv 2306.05535 ↗First posted 24 May 2023cs.CL cs.AI cs.IRcited by 7

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Abstract (arXiv)

Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers. While previous work on this problem has focused exclusively on the text modality, here we explore the utility of the audio modality as an additional input. We create a new multimodal dataset (text and audio in English) containing 48 hours of speech from past political debates in the USA. We then experimentally demonstrate that, in the case of multiple speakers, adding the audio modality yields sizable improvements over using the text modality alone; moreover, an audio-only model could outperform a text-only one for a single speaker. With the aim to enable future research, we make all our data and code publicly available at https://github.com/petar-iv/audio-checkworthiness-detection.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

15 of 36 references.

References · 15

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3 more
  1. 2019It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Predictioncited by 14
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.