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

Mélissa Bret
Master's Research - Université de Montréal
Roxanne Corriveau
Research Intern - Université de Montréal
Co-supervisor :
Reihaneh Rabbany
Anne Imouza
Master's Research - McGill University
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

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents
Maximilian Puelma Touzel
Sneheel Sarangi
Gayatri K
Busra Tugce Gurbuz
Austin Welch
Zachary Yang
Andreea Musulan
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Camille Thibault
Reihaneh Rabbany
Jean-François Godbout
Dan Zhao
Kellin Pelrine
The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (see more)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.
2025-08-16
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)
doi.org
Veracity: An Open-Source AI Fact-Checking System.
Taylor Lynn Curtis
Maximilian Puelma Touzel
William Garneau
Manon Gruaz
Mike Pinder
Li Wei Wang
Sukanya Krishna
Luda Cohen
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
2025-08-16
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)
doi.org
Veracity: An Open-Source AI Fact-Checking System
Taylor Lynn Curtis
Maximilian Puelma Touzel
William Garneau
Manon Gruaz
Mike Pinder
Li Wei Wang
Sukanya Krishna
Luda Cohen
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (see more) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.
2025-08-16
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)
doi.org
arxiv.org
A Guide to Misinformation Detection Data and Evaluation
Camille Thibault
Jacob-Junqi Tian
Gabrielle Péloquin-Skulski
Taylor Lynn Curtis
James Zhou
Florence Laflamme
Luke Yuxiang Guan
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (published)
doi.org
A Guide to Misinformation Detection Data and Evaluation
Camille Thibault
Jacob-Junqi Tian
Gabrielle Péloquin-Skulski
Taylor Lynn Curtis
James Zhou
Florence Laflamme
Yuxiang Guan
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (see more)em, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims, as well as the 9 datasets that consists of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at [anonymized].
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (published)
doi.org
openreview.net
Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest
Sahar Omidi Shayegan
Jean-François Godbout
Reihaneh Rabbany
Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (see more)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.
2025-07-26
colmweb.org/COLM/2025/Workshop/NLPOR (published)
openreview.net
openreview.net
Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest
Sahar Omidi Shayegan
Jean-François Godbout
Reihaneh Rabbany
Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (see more)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.
2025-07-26
colmweb.org/COLM/2025/Workshop/NLPOR (published)
openreview.net
openreview.net
TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection
Hao Yu
Shenyang Huang
Zachary Yang
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
Misinformation detection presents a significant challenge due to its knowledge-intensive and reasoning-intensive nature. While Retrieval-Aug… (see more)mented Generation (RAG) systems offer a promising direction, the effectiveness of their retrieval and reranking components is crucial. This paper introduces TRUTH, a novel reranking approach designed for domain adaptation, specifically for misinformation detection, which employs a two-stage training methodology: Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). We demonstrate that our 1B parameter TRUTH model achieves strong performance comparable to 7B models on established misinformation benchmarks such as FEVER and Canadian bilingual news datasets, improving retrieval quality and positively impacting downstream task accuracy. Our findings highlight the efficacy of combining SFT for broad knowledge acquisition and domain adaptation with DPO for nuanced reasoning alignment in developing efficient and effective rerankers for complex, knowledge-intensive tasks. Datasets and code will be available with the camera-ready version of the paper.
2025-07-25
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection
Hao Yu
Shenyang Huang
Zachary Yang
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
2025-07-25
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
Veracity: An Open-Source AI Fact-Checking System
Taylor Lynn Curtis
Maximilian Puelma Touzel
William Garneau
Manon Gruaz
Mike Pinder
Li Wei Wang
Sukanya Krishna
Luda Cohen
Jean-François Godbout
Reihaneh Rabbany
Kellin Pelrine
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (see more) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.
2025-06-18
ArXiv (preprint)
arxiv.org
arxiv.org
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
Matthew Kowal
Jasper Timm
Jean-François Godbout
Thomas H Costello
Antonio A. Arechar
Gordon Pennycook
David Rand
Adam Gleave
Kellin Pelrine
Persuasion is a powerful capability of large language models (LLMs) that both enables beneficial applications (e.g. helping people quit smok… (see more)ing) and raises significant risks (e.g. large-scale, targeted political manipulation). Prior work has found models possess a significant and growing persuasive capability, measured by belief changes in simulated or real users. However, these benchmarks overlook a crucial risk factor: the propensity of a model to attempt to persuade in harmful contexts. Understanding whether a model will blindly ``follow orders'' to persuade on harmful topics (e.g. glorifying joining a terrorist group) is key to understanding the efficacy of safety guardrails. Moreover, understanding if and when a model will engage in persuasive behavior in pursuit of some goal is essential to understanding the risks from agentic AI systems. We propose the Attempt to Persuade Eval (APE) benchmark, that shifts the focus from persuasion success to persuasion attempts, operationalized as a model's willingness to generate content aimed at shaping beliefs or behavior. Our evaluation framework probes frontier LLMs using a multi-turn conversational setup between simulated persuader and persuadee agents. APE explores a diverse spectrum of topics including conspiracies, controversial issues, and non-controversially harmful content. We introduce an automated evaluator model to identify willingness to persuade and measure the frequency and context of persuasive attempts. We find that many open and closed-weight models are frequently willing to attempt persuasion on harmful topics and that jailbreaking can increase willingness to engage in such behavior. Our results highlight gaps in current safety guardrails and underscore the importance of evaluating willingness to persuade as a key dimension of LLM risk. APE is available at github.com/AlignmentResearch/AttemptPersuadeEval
2025-06-03
ArXiv (preprint)
arxiv.org
arxiv.org
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
Matthew Kowal
Jasper Timm
Jean-François Godbout
Thomas H Costello
Antonio A. Arechar
Gordon Pennycook
David G. Rand
Adam Gleave
Kellin Pelrine
Persuasion is a powerful capability of large language models (LLMs) that both enables beneficial applications (e.g. helping people quit smok… (see more)ing) and raises significant risks (e.g. large-scale, targeted political manipulation). Prior work has found models possess a significant and growing persuasive capability, measured by belief changes in simulated or real users. However, these benchmarks overlook a crucial risk factor: the propensity of a model to attempt to persuade in harmful contexts. Understanding whether a model will blindly ``follow orders'' to persuade on harmful topics (e.g. glorifying joining a terrorist group) is key to understanding the efficacy of safety guardrails. Moreover, understanding if and when a model will engage in persuasive behavior in pursuit of some goal is essential to understanding the risks from agentic AI systems. We propose the Attempt to Persuade Eval (APE) benchmark, that shifts the focus from persuasion success to persuasion attempts, operationalized as a model's willingness to generate content aimed at shaping beliefs or behavior. Our evaluation framework probes frontier LLMs using a multi-turn conversational setup between simulated persuader and persuadee agents. APE explores a diverse spectrum of topics including conspiracies, controversial issues, and non-controversially harmful content. We introduce an automated evaluator model to identify willingness to persuade and measure the frequency and context of persuasive attempts. We find that many open and closed-weight models are frequently willing to attempt persuasion on harmful topics and that jailbreaking can increase willingness to engage in such behavior. Our results highlight gaps in current safety guardrails and underscore the importance of evaluating willingness to persuade as a key dimension of LLM risk. APE is available at github.com/AlignmentResearch/AttemptPersuadeEval
2025-06-01
arXiv (published)
doi.org
arxiv.org