Biased Echoes: Unraveling Mechanisms of Opinion Dynamics and their Impacts in Online Social Networks

The societal role played by public and individual opinions is crucial, not only because they shape our culture but also because they drive individual and - indirectly - collective actions, by influencing political decisions. Hence, understanding how opinions form and evolve is a problem that needs to be addressed and solved.Despite skepticism and contrasting empirical insights, the digital era lead to additional complexities into this process and posed a threat for an healthy process of opinion evolution, contributing to the creation and maintenance of polluted information environments. In this thesis, therefore, our aim was to investigate the interplay of biases and network effects in driving opinion formation and diffusion in online social networks. We first review the state-of-the-art in computational social sciences, focusing on the structures used to model societies, such as graphs, temporal graphs, and higher-order structures. We then delve into the milestones of opinion dynamics, discussing models that account for the impact of different underlying structures and characteristic elements of the digital space, such as algorithmic bias.The literature on opinion dynamics is wide, ranging from binary opinions and pair-wise interactions models to continuous opinions on time-evolving higher-order systems, in a never-ending effort to reduce the gap between reality and the models' predictions. However, despite such a rich set of mathematical studies, the works concerning the validation of models on real data are scarce.Our approach was therefore two-fold: first, we developed models of opinion dynamics that incorporate specific characteristics of the opinion formation process and simulate their long-term consequences; additionally, we employ a hybrid approach that leverages existing opinion dynamics models to estimate the level of "open-mindedness" within real online discussions.Lastly, we employed such methodology to "validate" one of the proposed model on a real online discussion. Throughout the work our main focus is to use the simplicity and interpretability of opinion dynamics model to better understand such a complex real phenomena.