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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11753 |
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| _version_ | 1866918045138550784 |
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| author | van der Meer, Michiel Korshunov, Pavel Marcel, Sébastien van der Plas, Lonneke |
| author_facet | van der Meer, Michiel Korshunov, Pavel Marcel, Sébastien van der Plas, Lonneke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims van der Meer, Michiel Korshunov, Pavel Marcel, Sébastien van der Plas, Lonneke Artificial Intelligence 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. |
| title | HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11753 |