What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study
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Computer Science > Computer Vision and Pattern Recognition
Title:What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study
Abstract:Multimodal misinformation is increasingly crafted to look convincing by pairing a textual claim with an image that appears to "prove" it. Yet in practice, building effective detectors often hinges on a small set of design choices that are rarely examined in a controlled way. In this paper, we conduct a large-scale study of multimodal design choices for misinformation detection with over 3,375 experiments- spanning three benchmark datasets and a broad range of pre-trained vision and language backbones. Through systematic comparisons and targeted robustness analyses, we distill practical guidance on which design choices help, when do they fail silently, and what aspects of the pipeline most strongly shape model behavior, answering 4 key Research Questions (RQs). We aim to provide a reliable foundation for designing stronger and more dependable multimodal misinformation detection systems, thus contributing to the broader research community.
| Comments: | Accepted at the Tenth Widening NLP Workshop (WiNLP), co-located with EMNLP 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM) |
| Cite as: | arXiv:2609.30402 [cs.CV] |
| (or arXiv:2609.30402v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30402
arXiv-issued DOI via DataCite (pending registration)
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