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Manuel Tonneau

University of Oxford

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#natural language process... Preprint Sep 2026

SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results

SWARM (Search-Web documents Annotated for Russian propaganda, Multilingual), a dataset of 2,183 search engine results across nine languages and diverse web domains, each annotated by trained coders for whether it supports a recurring Russian propaganda narrative is introduced.

Manuel Tonneau, A. Dubey, Farhan Shaikh et al. · 0 citations

LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages

Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect ge...

Sharif Kazemi, Tanya Popli, Neil K. R. Sehgal et al. · 0 citations

The Enforcement and Feasibility of Hate Speech Moderation on Twitter

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 at scale, remain poorly understood. We audit hate speech moderation on Twitter (now X) using 540,000 tweets annotated by trained native spea...

Manuel Tonneau, D. Thurgood, Diyi Liu et al. · 2 citations

Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructure

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 this case, we argue that a research field must build and govern its own measurement infrastructure rather than borrow it. Surveying 241 papers...

David Hartmann, Manuel Tonneau, Angelie Kraft et al. · 0 citations
#natural language process... Preprint Jan 2026

Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias

It is suggested that demographic conditioning in LLMs is not a cue-invariant category-level parameter but depends fundamentally on how identity is cued, reflecting responses to linguistic signals rather than stable demographic categories.

Manuel Tonneau, Neil K. R. Seghal, Niyati Malhotra et al. · 4 citations

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