17 citations · 24 across the 13 of their papers we have counts for
16 papers
GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria
Luise Koch, Manuel Tonneau, Niyati Malhotra +5
Research on gender-related online content is often operationalized through harm-centered tasks that collapse non-harmful gender-related and unrelated content, while existing datase…
AI-Assisted Submissions in Online Research Are Rare and Highly Concentrated but Routinely Approved
Neil K. R. Sehgal, Manuel Tonneau, Dunigan Folk +2
Online research platforms underpin much of what science claims about people, on the assumption that a human produced each response. Generative AI threatens that assumption by letti…
SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results
Manuel Tonneau, Abhinav Dubey, Farhan Shaikh +8
Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social media, in one or two languages,…
LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages
Sharif Kazemi, Tanya Popli, Neil K. R. Sehgal +7
Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-bas…
Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructure
David Hartmann, Manuel Tonneau, Angelie Kraft +7
Perspective API closes at the end of 2026, removing the de facto standard for toxicity measurement and exposing researchers' dependence on a tool they did not control. Drawing on t…
The Enforcement and Feasibility of Hate Speech Moderation
Manuel Tonneau, Dylan Thurgood, Diyi Liu +7
Online hate speech is associated with harms ranging from deteriorating mental health to violence, yet how consistently platforms moderate hate, and whether enforcement is feasible…
Celebrity messages reduce online hate and limit its spread
Eaman Jahani, Blas Kolic, Manuel Tonneau +5
Online hate spreads rapidly, yet little is known about whether preventive and scalable strategies can curb it. We conducted the largest randomized controlled trial of hate speech p…
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra +7
Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This ap…
Effects of Static and Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Clinical Trial
Neil K. R. Sehgal, Manuel Tonneau, Andy SL Tan +6
Large language model chatbots show increasing promise in persuasive communication, but their clinical utility remains uncertain, particularly in settings where sustained conversati…
Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial
Neil K. R. Sehgal, Sunny Rai, Manuel Tonneau +6
Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant…
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
Jabez Magomere, Emanuele La Malfa, Manuel Tonneau +2
Online misinformation remains a critical challenge, and fact-checkers increasingly rely on claim matching systems that use sentence embedding models to retrieve relevant fact-check…
HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter
Manuel Tonneau, Diyi Liu, Niyati Malhotra +4
To address the global challenge of online hate speech, prior research has developed detection models to flag such content on social media. However, due to systematic biases in eval…
From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets
Manuel Tonneau, Diyi Liu, Samuel Fraiberger +3
Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural bias…
NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data
Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri +4
To address the global issue of online hate, hate speech detection (HSD) systems are typically developed on datasets from the United States, thereby failing to generalize to English…
Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models
Khyati Khandelwal, Manuel Tonneau, Andrew M. Bean +2
Large Language Models (LLMs), now used daily by millions, can encode societal biases, exposing their users to representational harms. A large body of scholarship on LLM bias exists…
Multilingual Detection of Personal Employment Status on Twitter
Manuel Tonneau, Dhaval Adjodah, João Palotti +2
Detecting disclosures of individuals' employment status on social media can provide valuable information to match job seekers with suitable vacancies, offer social protection, or m…