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Fake news spreading, with the aim of manipulating individuals' perceptions offacts, is now recognized as a major problem in many democratic societies. Yet,to date, little has been understood about how fake news spreads on socialnetworks, what the influence of the education level of individuals is, whenfake news is effective in influencing public opinion, and what interventionsmight be successful in mitigating their effect. In this paper, starting fromthe recently introduced kinetic multi-agent model with competence by the firsttwo authors, we propose to derive reduced-order models through the notion ofsocial closure in the mean-field approximation that has its roots in theclassical hydrodynamic closure of kinetic theory. This approach allows toobtain simplified models in which the competence and learning of the agentsmaintain their role in the dynamics and, at the same time, the structure ofsuch models is more suitable to be interfaced with data-driven applications.Examples of different Twitter-based test cases are described and discussed.

From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications