16 citations · 26 across the 20 of their papers we have counts for
21 papers
EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations
Sneheel Sarangi, Maximilian Puelma Touzel, Aurélien Bück-Kaeffer +3
LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack of architectural standardization prevent…
The Cookbook: Design Space of LLM-based Social Simulations
Aurélien Bück-Kaeffer, Sneheel Sarangi, Maximilian Puelma Touzel +3
Studies attempting to simulate human behavior with grow in numbers while LLM-only social networks have started appearing outside of controlled settings…
What do people want to fact-check?
Bijean Ghafouri, Dorsaf Sallami, Luca Luceri +4
Research on misinformation has focused almost exclusively on supply, asking what falsehoods circulate, who produces them, and whether corrections work. A basic demand-side question…
Large language models can effectively convince people to believe conspiracies
Thomas H. Costello, Kellin Pelrine, Matthew Kowal +6
Large language models (LLMs) have been shown to be persuasive across a variety of contexts. But it remains unclear whether this persuasive power advantages accuracy, or if bad acto…
Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics
Victor Livernoche, Andreea Musulan, Zachary Yang +2
Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circulate across social platforms during maj…
OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection
Victor Livernoche, Akshatha Arodi, Andreea Musulan +5
Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically sensitive contexts. This challenge is a…
: A Social Media User Dataset for LLM Persona Evaluation and Training
Aurélien Bück-Kaeffer, Je Qin Chooi, Dan Zhao +5
Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethically or logistically challenging with h…
CrediBench: Building Web-Scale Network Datasets for Information Integrity
Emma Kondrup, Sebastian Sabry, Hussein Abdallah +9
Automatically assessing the credibility of online sources presents an invaluable tool for navigating today's information ecosystem. However, existing approaches either depend on sc…
Veracity: An Open-Source AI Fact-Checking System
Taylor Lynn Curtis, Maximilian Puelma Touzel, William Garneau +8
The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI…
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
Matthew Kowal, Jasper Timm, Jean-Francois Godbout +6
Persuasion is a powerful capability of large language models (LLMs) that both enables beneficial applications (e.g. helping people quit smoking) and raises significant risks (e.g.…
From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
Ruben Weijers, Denton Wu, Hannah Betts +10
Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independ…
Online Influence Campaigns: Strategies and Vulnerabilities
Andreea Musulan, Veronica Xia, Ethan Kosak-Hine +5
In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, societal-scale manipulation by malicious act…
Epistemic Integrity in Large Language Models
Bijean Ghafouri, Shahrad Mohammadzadeh, James Zhou +8
Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks fo…
A Guide to Misinformation Detection Data and Evaluation
Camille Thibault, Jacob-Junqi Tian, Gabrielle Peloquin-Skulski +7
Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we have curated the largest collection of (m…
Web Retrieval Agents for Evidence-Based Misinformation Detection
Jacob-Junqi Tian, Hao Yu, Yury Orlovskiy +7
This paper develops an agent-based automated fact-checking approach for detecting misinformation. We demonstrate that combining a powerful LLM agent, which does not have access to…
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 +6
Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded specific events, such as when and which…
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation
Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany +1
Large Language Models have emerged as prime candidates to tackle misinformation mitigation. However, existing approaches struggle with hallucinations and overconfident predictions.…
Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation
Tyler Vergho, Jean-Francois Godbout, Reihaneh Rabbany +1
Recent large language models (LLMs) have been shown to be effective for misinformation detection. However, the choice of LLMs for experiments varies widely, leading to uncertain co…
Uncertainty Resolution in Misinformation Detection
Yury Orlovskiy, Camille Thibault, Anne Imouza +3
Misinformation poses a variety of risks, such as undermining public trust and distorting factual discourse. Large Language Models (LLMs) like GPT-4 have been shown effective in mit…
Party Prediction for Twitter
Kellin Pelrine, Anne Imouza, Zachary Yang +8
A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their polit…
Open, Closed, or Small Language Models for Text Classification?
Hao Yu, Zachary Yang, Kellin Pelrine +2
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks. But many questions remain, including whether open-source models mat…