Fake News and Post-truth: Numerical Simulations of Information Diffusion in Social Networks

Abstract

The year 2016 was crucial in terms of the use of social networks as tools for disseminating information for political purposes. The election of candidate Donald Trump for the presidency of the United States of America lit a warning signal about the influence that the information disseminated through social networks exerted in the choice of candidates by the American people. Since then, researchers from different areas have focused on the topic, which involves different aspects: computing, social sciences, mathematics, among others. Therefore, the object of study of this work is the phenomenon of behavioral changes brought about by the new social relationships established by digital social networks, under the scope of the spread of fake news through them. To guarantee the intended study, the general objective was to adapt mathematical models consisting of ordinary differential equations for the dissemination of information on social networks for the spread of fake news. As specific objectives, the contribution of the mathematical models proposed in the mitigation, through algorithms, of the spread of false or distorted information was discussed, as well as the discussion on the concepts of fake news and post-truth from a social point of view, in a way that individuals can also distinguish true information from disinformation through individual interpretation tools. As a research methodology, bibliographic research was chosen and a systematic literature review was carried out, to consider published works on the proposed research object. For the numerical simulations, a numerical code was developed in MATLAB, which was able to carry out the desirable experiments. It is concluded that innovation diffusion models can adapt to fake news dissemination models. However, such models are not able to robustly simulate the mitigation of fake news.