NewsClaims: A New Benchmark for Claim Detection from News with Background Knowledge

Abstract

Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating news misinformation. However, most existing work has focused on claim sentence analysis while overlooking crucial background attributes (e.g., claimer, claim objects). In this work, we present NewsClaims, a new benchmark for knowledge-aware claim detection in the news domain. We redefine the claim detection problem to include extraction of additional background attributes related to each claim and release 889 claims annotated over 143 news articles. NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. To this end, we provide a comprehensive evaluation of zero-shot and prompt-based baselines for NewsClaims.

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