Portrait of Jean-François Godbout

Jean-François Godbout

Associate Academic Member
Full Professor, Université de Montréal, Department of Political Science
Alumni
Research Topics
AI Safety
Disinformation
Generative Models
Google Scholar
LinkedIn

Biography

Jean-François Godbout is a professor at the Université de Montréal in the Department of Political Science and an Associate Academic Member at Mila - Quebec Artificial Intelligence Institute. His research is primarily focused on computational social science, AI safety, and the impact of generative AI on society. He is currently Director of the Data analysis undergraduate program in social sciences and humanities at the Université de Montréal and a researcher at IVADO.

Current Students

Andreea Musulan
Postdoctorate - Université de Montréal
Julien Robin
PhD - Université de Montréal
Google Scholar
Camille Thibault
Master's Research - Université de Montréal
Co-supervisor :
Reihaneh Rabbany
Website
Github
Google Scholar
Sveta Zhuk
Master's Research - Université de Montréal
Co-supervisor :
Reihaneh Rabbany

Publications

Online Partisan Polarization of COVID-19
Zachary Yang
Anne Imouza
Kellin Pelrine
Sacha Lévy
Jiewen Liu
Gabrielle Desrosiers-Brisebois
Jean-François Godbout
André Blais
Reihaneh Rabbany
In today’s age of (mis)information, many people utilize various social media platforms in an attempt to shape public opinion on several im… (see more)portant 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.
2021-12-01
2021 International Conference on Data Mining Workshops (ICDMW) (published)
doi.org