🤖 AI Summary
This study addresses the lack of systematic validation regarding design choices in multimodal fake news detection by conducting a large-scale empirical investigation. Leveraging pretrained vision-language backbones across multiple benchmark datasets, we perform over 3,375 controlled experiments to systematically evaluate model design decisions and their robustness. Our analysis identifies the core factors influencing model behavior, distills key design guidelines, and reveals typical failure modes. By rigorously examining these architectural and training considerations, this work establishes a solid foundation for developing reliable multimodal fake news detection systems.
📝 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.