  To mitigate the negative effects of false information more effectively, the
development of automated AI (artificial intelligence) tools assisting
fact-checkers is needed. Despite the existing research, there is still a gap
between the fact-checking practitioners' needs and pains and the current AI
research. We aspire to bridge this gap by employing methods of information
behavior research to identify implications for designing better human-centered
AI-based supporting tools.
  In this study, we conducted semi-structured in-depth interviews with Central
European fact-checkers. The information behavior and requirements on desired
supporting tools were analyzed using iterative bottom-up content analysis,
bringing the techniques from grounded theory. The most significant needs were
validated with a survey extended to fact-checkers from across Europe, in which
we collected 24 responses from 20 European countries, i.e., 62% active European
IFCN (International Fact-Checking Network) signatories.
  Our contributions are theoretical as well as practical. First, by being able
to map our findings about the needs of fact-checking organizations to the
relevant tasks for AI research, we have shown that the methods of information
behavior research are relevant for studying the processes in the organizations
and that these methods can be used to bridge the gap between the users and AI
researchers. Second, we have identified fact-checkers' needs and pains focusing
on so far unexplored dimensions and emphasizing the needs of fact-checkers from
Central and Eastern Europe as well as from low-resource language groups which
have implications for development of new resources (datasets) as well as for
the focus of AI research in this domain.
