Annotation Tool and Dataset for Fact-Checking Podcasts

Vinay Setty; Adam James Becker

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arXiv · 2502.01402

Annotation Tool and Dataset for Fact-Checking Podcasts

Vinay Setty·Adam James Becker

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Abstract

Podcasts are a popular medium on the web, featuring diverse and multilingual content that often includes unverified claims. Fact-checking podcasts is a challenging task, requiring transcription, annotation, and claim verification, all while preserving the contextual details of spoken content. Our tool offers a novel approach to tackle these challenges by enabling real-time annotation of podcasts during playback. This unique capability allows users to listen to the podcast and annotate key elements, such as check-worthy claims, claim spans, and contextual errors, simultaneously. By integrating advanced transcription models like OpenAI's Whisper and leveraging crowdsourced annotations, we create high-quality datasets to fine-tune multilingual transformer models such as XLM-RoBERTa for tasks like claim detection and stance classification. Furthermore, we release the annotated podcast transcripts and sample annotations with preliminary experiments.

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Vinay Setty, Adam James Becker. 2025-02-03. Annotation Tool and Dataset for Fact-Checking Podcasts. https://arxiv.org/abs/2502.01402

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