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Information × Registration Number 2123U011189, Article popup.category Препринт Title Fake News Epidemic Model Development (AI translated) popup.author Yasinovskyi PavloYasinovskyi Pavlo popup.publication 01-01-2023 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4421 popup.publisher Description As the commonness of fake news continues to grow, understanding its spread and impact on social networks is of paramount importance. This paper presents a C++ implementation of a previously described SIR-like Susceptible-Infected-Fact-Checker (SIFC) model to study the dynamics of fake news spread. Our solution provides sig- nificant performance enabling researchers to simulate massive social networks and examine how different variables affect the propagation of fake news. In order to assess how accurately the false news epidemic in a social network is depicted, we compare our model with real-world data. Our research provides useful information that authorities, social media managers, and users may utilize to build policies that will stop the spread of fake news and foster a more positive online community. popup.nrat_date 2025-11-05 Close