5 papers · 1 filter
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…
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…
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…
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…
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…