The role of open-mindedness in shaping opinions: theory and empirical results

A model of opinion dynamics featuring disagreement and biased interactions, which reproduces the phenomenon of echo-chambers, is considered. An improvement of the model presenting open-minded agents, i.e. not acting in a biased way, and their ability to dismantle echo-chambers is also treated. In this setting, we devise a mechanism introducing open-mindedness in a self-consistent way. We also study how open-minded behavior can be enhanced through different mechanisms, leading to the disruption of the echo-chambers. In a second part, we consider a dataset consisting of six months of tweets on italian politics. We build a complete pipeline featuring machine learning and simple natural language processing tools in order to analyse the data. The goal is to empirically measure the observables of the model, in order to test it against empirical data but also to gain more insights on the users' opinion dynamics. We find that the model is only able to partially reproduce the properties of the dataset, even though some limitations of our methods prevent us from too strong conclusions. Still, the tools provided by the model are useful in determining some features of social bot users and their difference from human users, giving also some insights on the italian public political debate.