Clowns in the Visual Artists:

Abstract

This case study explores the challenges of defining a data set to analyze changes in Wikipedia’s gender gap for articles about visual art. Wikipedia and Wikidata each group content in data structures that produce overlapping and incomplete visual art datasets and include non-visual art data. To circumvent potentially biased editorial decisions about what to include or exclude, this case study describes the process of using a topic model algorithm that identifies a dataset by analyzing the words in each article and grouping the articles into topics.