Portrait de Jean-François Godbout

Jean-François Godbout

Membre académique associé
Professeur titulaire, Université de Montréal, Département de science politique
Alumni
Site web
Google Scholar

Biographie

Jean-François Godbout est professeur au département de science politique de l'Université de Montréal et membre académique associé à Mila - Institut québécois d'intelligence artificielle. Ses recherches portent principalement sur les sciences sociales computationnelles, la sécurité de l'IA et l'impact de l'IA générative sur la société. Il est actuellement directeur du programme de premier cycle en analyse des données en sciences humaines à l'Université de Montréal et chercheur à IVADO.

Étudiants actuels

Andreea Musulan
Postdoctorat - UdeM
Julien Robin
Doctorat - UdeM
Google Scholar
Camille Thibault
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Reihaneh Rabbany
Site web
Github
Google Scholar
Sveta Zhuk
Maîtrise recherche - UdeM
Co-superviseur⋅e :
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… (voir plus)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) (publié)
doi.org