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: C Diebel, M Goutier, M Adam, A Benlian
Year: 2025
Published in: Business & Information Systems ..., 2025 - Springer
Institution: Technical University of Darmstadt, University of Goettingen
Research Area: Human-AI Collaboration, System Satisfaction, User Competence
Discipline: Information Systems, Human-Computer Interaction, Artificial Intelligence
Proactive AI-based agent assistance decreases users' competence-based self-esteem and system satisfaction, especially for users with higher AI knowledge.
Methods: Vignette-based online experiment using self-determination theory as the framework to evaluate user responses to proactive vs. reactive AI assistance.
Key Findings: Impact of proactive vs. reactive AI help on users' competence-based self-esteem and system satisfaction, moderated by users' AI knowledge levels.
DOI: https://doi.org/10.1007/s12599-024-00918-y
Citations: 32
