TY - JOUR
T1 - Modeling opinion polarization on social media
T2 - Application to Covid-19 vaccination hesitancy in Italy
AU - Franceschi, Jonathan
AU - Bresadola, Marco
AU - Pareschi, Lorenzo
AU - Bellodi, Elena
AU - Gavanelli, Marco
N1 - Funding Information:
MB is the recipient of funding from the FIR 2021 project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work has been written within the activities of GNFM and GNCS groups of INdAM (National Institute of High Mathematics).
Publisher Copyright:
© 2023 Franceschi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/10/2
Y1 - 2023/10/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85173060916&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0291993
DO - 10.1371/journal.pone.0291993
M3 - Article
C2 - 37782677
AN - SCOPUS:85173060916
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 10
M1 - e0291993
ER -
