arXiv:2510.23508v3 Announce Type: replace Abstract: Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover on only one or two languages, focus only on one task, or rely on external news article sets for sourcing true claims. To address these shortcomings, we introduce M4FC, a new real-world dataset comprising 4,982 images paired with 6,980 claims. The images, verified by professional fact-checkers from 22 organizations, represent a diverse range of cultural and geographic contexts. Each claim is available in one or two out of ten
The proliferation of AI-generated misinformation and deepfakes across diverse languages and cultures necessitates more robust and comprehensive fact-checking methodologies and datasets, driving the creation of M4FC.
This dataset significantly advances the capabilities for multimodal and multilingual AI fact-checking, crucial for combating disinformation campaigns and ensuring information integrity globally.
The availability of a large, diverse, and professionally verified dataset like M4FC provides an essential resource for training more effective AI models in identifying and debunking real-world misinformation across various contexts.
- · AI fact-checking platforms
- · Social media companies
- · Digital forensics researchers
- · International organizations combating misinformation
- · State-sponsored disinformation actors
- · Producers of deepfakes and fake news
Improved accuracy and efficiency of AI systems designed for multimodal and multilingual fact-checking.
Increased public trust in information on digital platforms as disinformation becomes harder to spread effectively.
Potential for new regulatory frameworks and international cooperation around AI-powered content verification standards.
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