In this thesis we analyze the ranking systems for fake news detec- tion. The dissertation is divided into 2 parts. In the theoretical and in the analysis of various models (FakerFact, Index, CSI, FNED) for de- tecting false news. In the theoretical part some definitions are men- tioned in order to make the subject more understandable, some im- portant events with fake news (2016 US Elections, 2017 French Elec- tions, 2020 Covid - 19) and finally an analysis is made for the EU, for the principles and values and how responded to the fake news chal- lenge. As far as the technical part, there is a lot of discussion in the literature about machine learning in fake news detection and in the latest models it seems to be taken into account. The article concludes that fake news can have devastating consequences for health and electoral issues. Also, no articles were found in the literature that combine something technical with something more theoretical, which means that the two sides may not communicate properly with each other.
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