Fake News Epidemic Model Development

dc.contributor.author	Yasinovskyi, Pavlo
dc.date.accessioned	2024-02-14T09:47:31Z
dc.date.available	2024-02-14T09:47:31Z
dc.date.issued	2023
dc.identifier.citation	Yasinovskyi, Pavlo. Fake News Epidemic Model Development / Pavlo Yasinovskyi; Supervisor: Jaroslav Ilnytskyi; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 34 p.: ill.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4421
dc.language.iso	en	uk
dc.title	Fake News Epidemic Model Development	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	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.	uk