Gender imbalance in Wikipedia content is a known challenge which the editor community is actively addressing. The aim of this paper is to provide the Wikipedia community with instruments to estimate the magnitude of the problem for different entity types (also known as classes) in Wikipedia. To this end, we apply class completeness estimation methods based on the gender attribute. Our results show not only which gender for different sub-classes of Person is more prevalent in Wikipedia, but also an idea of how complete the coverage is for difference genders and sub-classes of Person.
@article{arxiv.2401.08993,
title = {Estimating Gender Completeness in Wikipedia},
author = {Hrishikesh Patel and Tianwa Chen and Ivano Bongiovanni and Gianluca Demartini},
journal= {arXiv preprint arXiv:2401.08993},
year = {2024}
}