Anne Imouza

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

Zachary Yang

Anne Imouza

Maximilian Puelma Touzel

C'ecile Amadoro

Gabrielle Desrosiers-Brisebois

Kellin Pelrine

Sacha Lévy

Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (voir plus)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.

2024-07-03

ArXiv (prépublication)

Uncertainty Resolution in Misinformation Detection

Yury Orlovskiy

Camille Thibault

Anne Imouza

Kellin Pelrine

2024-01-02

ArXiv (prépublication)

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4

Kellin Pelrine

Anne Imouza

Meilina Reksoprodjo

Camille Thibault

Caleb Gupta

Joel Christoph

Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on… (voir plus) generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible. We first demonstrate that GPT-4 can outperform prior methods in multiple settings and languages. Next, we explore generalization, revealing that GPT-4 and RoBERTa-large exhibit differences in failure modes. Third, we propose techniques to handle uncertainty that can detect impossible examples and strongly improve outcomes. We also discuss results on other language models, temperature, prompting, versioning, explainability, and web retrieval, each one providing practical insights and directions for future research. Finally, we publish the LIAR-New dataset with novel paired English and French misinformation data and Possibility labels that indicate if there is sufficient context for veracity evaluation. Overall, this research lays the groundwork for future tools that can drive real-world progress to combat misinformation.

2023-10-07

EMNLP/2023/Conference (accepté)

openreview.net

Party Prediction for Twitter

Sacha Lévy

Gabrielle Desrosiers-Brisebois

Aarash Feizi

C'ecile Amadoro

André Blais

2023-08-25

ArXiv (prépublication)

Online Partisan Polarization of COVID-19

Sacha Lévy

Gabrielle Desrosiers-Brisebois

André Blais

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)