%Aigaion2 BibTeX export from Idiap Publications
%Friday 05 December 2025 11:23:09 AM

@INPROCEEDINGS{vanderMeer_ACL2025_2025,
                      author = {van der Meer, Michiel and Korshunov, Pavel and Marcel, S{\'{e}}bastien and van der Plas, Lonneke},
                    projects = {Idiap, FACTCHECK},
         mainresearchprogram = {Sustainable & Resilient Societies},
                       month = jul,
                       title = {HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims},
                   booktitle = {The 63rd Annual Meeting of the Association for Computational Linguistics},
                        year = {2025},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2025/vanderMeer_ACL2025_2025.pdf}
}