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To mitigate the negative effects of false information more effectively, thedevelopment of Artificial Intelligence (AI) systems assisting fact-checkers isneeded. Nevertheless, the lack of focus on the needs of these stakeholdersresults in their limited acceptance and skepticism toward automating the wholefact-checking process. In this study, we conducted semi-structured in-depthinterviews with Central European fact-checkers. Their activities and problemswere analyzed using iterative content analysis. The most significant problemswere validated with a survey of European fact-checkers, in which we collected24 responses from 20 countries, i.e., 62\% of active European signatories ofthe International Fact-Checking Network (IFCN).  Our contributions include an in-depth examination of the variability offact-checking work in non-English speaking regions, which still remainedlargely uncovered. By aligning them with the knowledge from prior studies, wecreated conceptual models that help understand the fact-checking processes.Thanks to the interdisciplinary collaboration, we extend the fact-checkingprocess in AI research by three additional stages. In addition, we mapped ourfindings on the fact-checkers' activities and needs to the relevant tasks forAI research. The new opportunities identified for AI researchers and developershave implications for the focus of AI research in this domain.

Autonomation, not Automation: Activities and Needs of Fact-checkers as a Basis for Designing Human-Centered AI Systems