Opinion dynamic modeling of news perception

During the last decade, the advent of the Web and online social networks rapidly changed the way we were used to search, gather and discuss information of any kind. These tools have given everyone the chance to become a news medium. While promoting more democratic access to information, direct and unfiltered communication channels may increase our chances to confront malicious/misleading behavior. Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several perspectives; among such vast literature, an important theme is the analysis of fake news' perception. In this work, moving from such observation, I propose a family of opinion dynamics models to understand the role of specific social factors on the acceptance/rejection of news contents. In particular, I model and discuss the effect that stubborn agents, different levels of trust among individuals, open-mindedness, attraction/repulsion phenomena, and similarity between agents have on the population dynamics of news perception. To discuss the peculiarities of the proposed models, I tested them on two synthetic network topologies thus underlying when/how they affect the stable states reached by the performed simulations.