Online Partisan Polarization of COVID-19
Zachary Yang, Anne Imouza, Kellin Pelrine, Sacha Lévy, Jiewen Liu, Gabrielle Desrosiers-Brisebois, J. Godbout, A. Blais, Reihaneh Rabbany
2021 International Conference on Data Mining Workshops (ICDMW)
Abstract
In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several important issues, including elections and the COVID-19 pandemic. These two topics have recently become intertwined given the importance of complying with public health measures related to COVID-19 and politicians’ management of the pandemic. Motivated by this, we study the partisan polarization of COVID-19 discussions on social media. We propose and utilize a novel measure of partisan polarization to analyze more than 380 million posts from Twitter and Parler around the 2020 US presidential election. We find strong correlation between peaks in polarization and polarizing events, such as the January 6th Capitol Hill riot. We further classify each post into key COVID-19 issues of lockdown, masks, vaccines, as well as miscellaneous, to investigate both the volume and polarization on these topics and how they vary through time. Parler includes more negative discussions around lockdown and masks, as expected, but not much around vaccines. We also observe more balanced discussions on Twitter and a general disconnect between the discussions on Parler and Twitter.