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

TL;DR

This paper introduces a differential opinion dynamics model coupled with fake news dissemination to analyze polarization on social media, specifically applied to Covid-19 vaccination hesitancy in Italy, capturing bimodal opinion distributions.

Contribution

The work develops a novel coupled model integrating opinion formation and fake news spread, validated with social media sentiment data from Italy.

Findings

Model reproduces bimodal opinion distributions observed in data.

Numerical simulations match polarization patterns in vaccine hesitancy.

Framework demonstrates the impact of misinformation on opinion polarization.

Abstract

The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities.