HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
| Type of publication: | Conference paper |
| Citation: | vanderMeer_ACL2025_2025 |
| Booktitle: | The 63rd Annual Meeting of the Association for Computational Linguistics |
| Year: | 2025 |
| Month: | July |
| Abstract: | Misinformation can be countered with fact-checking, 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 text-based 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. |
| Main Research Program: | Sustainable & Resilient Societies |
| Keywords: | |
| Projects: |
Idiap FACTCHECK |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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