HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
M. van der Meer, Pavel Korshunov, Sébastien Marcel, Lonneke Van Der Plas · 2025
Misinformation can be countered with factchecking, but the process is costly and slow.Identifying checkworthy claims is the first step, where automation can help scale fact-checkers' efforts.However, detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic.We introduce HINTSOFTRUTH, a public dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs.The mix of real and synthetic data makes this dataset unique and ideal for benchmarking detection methods.We compare fine-tuned and prompted Large Language Models (LLMs).We find that well-configured lightweight textbased encoders perform comparably to multimodal models but the former only focus on identifying non-claim-like content.Multimodal LLMs can be more accurate but come at a significant computational cost, making them impractical for large-scale applications.When faced with synthetic data, multimodal models perform more robustly.