Authors: J Geng, J Tonglet, I Gurevych
Year: 2026
Published in: arXiv preprint arXiv:2510.23508, 2025•arxiv.org
Institution: KU Leuven, TU Darmstadt, Ubiquitous Knowledge Processing Lab, MBZUAI, ATHENE
Research Area: Human-Computer Interaction
Discipline: Machine Learning, Artificial Intelligence
M4FC is a new dataset that addresses limitations in existing multimodal fact-checking datasets by providing multilingual and multicultural claims verified by professional fact-checkers across six fact-checking tasks.
Methods: The dataset was created by pairing 4,982 images with 6,980 claims, which were verified by professional fact-checkers from 22 organizations covering diverse cultural and geographic contexts. The claims are available in up to ten languages and span six different multimodal fact-checking tasks.
Key Findings: The study measured the efficacy of the M4FC dataset across six multimodal fact-checking tasks, with a focus on how combining intermediate tasks affects the performance of verdict prediction.
Citations: 3
Sample Size: 6980
Authors: T Hu, N Collier
Year: 2025
Published in: arXiv preprint arXiv:2503.03335, 2025 - arxiv.org
Institution: University of Cambridge
Research Area: Affective Computing, Natural Language Processing, Computational Social Science
Discipline: Computational Social Science
The iNews dataset is a multimodal resource for studying personalized affective responses to news, improving modeling accuracy by incorporating annotator persona metadata.
Methods: 292 demographically diverse UK participants annotated 2,899 Facebook news posts with multidimensional labels (e.g., emotions, valence, arousal), combined with comprehensive participant persona data.
Key Findings: Modeled personalized affective responses to news through annotations capturing valence, arousal, emotions, and persona metadata.
Citations: 2
Sample Size: 2899
