ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos
This addresses a gap in misinformation detection for video content, which is incremental as it extends existing text-based methods to spoken transcripts.
The authors tackled the problem of detecting claims in multilingual video transcripts by introducing ViClaim, a dataset of 1,798 annotated transcripts across three languages and six topics, achieving up to 0.896 macro F1 in cross-validation but showing challenges in generalization to unseen topics.
The growing influence of video content as a medium for communication and misinformation underscores the urgent need for effective tools to analyze claims in multilingual and multi-topic settings. Existing efforts in misinformation detection largely focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. We introduce ViClaim, a dataset of 1,798 annotated video transcripts across three languages (English, German, Spanish) and six topics. Each sentence in the transcripts is labeled with three claim-related categories: fact-check-worthy, fact-non-check-worthy, or opinion. We developed a custom annotation tool to facilitate the highly complex annotation process. Experiments with state-of-the-art multilingual language models demonstrate strong performance in cross-validation (macro F1 up to 0.896) but reveal challenges in generalization to unseen topics, particularly for distinct domains. Our findings highlight the complexity of claim detection in video transcripts. ViClaim offers a robust foundation for advancing misinformation detection in video-based communication, addressing a critical gap in multimodal analysis.