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The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topicon which the public conversation is permeated by misleading claims, andthoughts tend to polarize, especially on online social networks. In this work,motivated by recent natural language processing techniques to systematicallyextract and quantify opinions from text messages, we present a differentialframework for bivariate opinion formation dynamics that is coupled with acompartmental model for fake news dissemination. Thanks to a mean-fieldanalysis we demonstrate that the resulting Fokker-Planck system permits toreproduce bimodal distributions of opinions as observed in polarizationdynamics. The model is then applied to sentiment analysis data from socialmedia platforms in Italy, in order to analyze the evolution of opinions aboutCovid-19 vaccination. We show through numerical simulations that the model iscapable to describe correctly the formation of the bimodal opinion structureobserved in the vaccine-hesitant dataset, which is witness of the knownpolarization effects that happen within closed online communities.

Modeling opinion polarization on social media: application to Covid-19 vaccination hesitancy in Italy