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Anne Imouza

anne.imouza@mila.quebec
Maîtrise recherche - McGill University

Publications

Uncertainty Resolution in Misinformation Detection
Yury Orlovskiy
Camille Thibault
Anne Imouza
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
2024-01-02
ArXiv (prépublication)
doi.org
arxiv.org
Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Kellin Pelrine
Anne Imouza
Meilina Reksoprodjo
Camille Thibault
Caleb Gupta
Joel Christoph
Reihaneh Rabbany
Jean-François Godbout
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)
doi.org
openreview.net
Party Prediction for Twitter
Kellin Pelrine
Anne Imouza
Zachary Yang
Jacob-Junqi Tian
Sacha Lévy
Gabrielle Desrosiers-Brisebois
Aarash Feizi
C'ecile Amadoro
André Blais
Jean-François Godbout
Reihaneh Rabbany
2023-08-25
ArXiv (prépublication)
doi.org
arxiv.org
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