Polarizing Opinion Dynamics with Confirmation Bias.

Social media and online networks have enabled discussions between users at a planetary scale on controversial topics. However, instead of seeing users converging to a consensus, they tend to partition into groups holding diametric opinions. In this work we propose an opinion dynamics model that starts from a given graph topology, and updates in each iteration both the opinions of the agents, and the listening structure of each agent, assuming there is confirmation bias. We analyze our model, both theoretically and empirically, and prove that it generates a listening structure that is likely to be polarized. We show a novel application of our model, specifically how it can be used to find polarized niches across different Twitter layers. Finally, we evaluate and compare our model to other polarization models on various synthetic datasets, showing that it yields equilibria with unique characteristics, including high polarization and low disagreement.