2021/06/03 by Jiao Sun, Nanyun Peng, Sun, Jiao +1 · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Cancer-related gene regulation #Computation and Language (cs.CL) #Computers and Society (cs.CY) #Event (particle physics) #FOS: Computer and information sciences #Foundation (evidence) #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Political science #Psychology #Social psychology #Wikis in Education and Collaboration #cs.AI #cs.CL #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.2106.01601
published in arXiv (Cornell University) (Cornell University) · ACL 2021
arxiv created 2021/06/03 · openalex publication_date 2021/06/03 · arxiv updated 2022/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.