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20232026
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cs.CL2026

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,…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2024

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…