WP:clubhouse?: an exploration of Wikipedia's gender imbalance

By Shyong K. Lam, A. Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loreen Terveen, John Riedl. 2011

232 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, Human-Computer Interaction, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Sociology and Political Science

In Human-Computer Interaction

In Social Sciences & Humanities

Citations in the corpus

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021, background): “Several works have shown that the distribution of demographics of Wikipedia authors is highly skewed resulting in various types of biases [9, 19, 36].”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, background): “[27] have studied gender imbalance in Wikipedia, and reported on how in some domains, such as movies, articles of particular interest to females were substantially less covered than articles of specific interest to males – a direct consequence of having very few female editors relative to male ones.”
First Women, Second Sex: Gender Bias in Wikipedia (2015, background): “Categories and Subject Descriptors H.3.4 [Information Storage and Retrieval]: Systems and Software—Information networks Keywords Wikipedia; Gender; Gender Bias; Computational Linguistics.”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “Studies show gender disparities on Wikipedia in content, contributors, and revisions for the top tier of Wikipedians (Wagner et al. 2015; Reagle and Rhue 2011; Lam et al. 2011; Antin et al. 2011; Lam et al. 2011).”
It’s going to be okay: Measuring Access to Support in Online Communities (2018, unknown): “This work connects with the growing body of computational studies of gender disparity in online behavior (e.g., Lam et al., 2011; Magno and Weber, 2014; Garimella and Mihalcea, 2016; Li et al., 2018); our work here examines this disparity along a new dimension, support, and unlike prior work,…”