Visualizing Wikidata: Using Python to Analyze Identity and Representation in Wikidata about Black Art Exhibitions

Abstract

For many of us, adding “wiki” to the end of a Google search is like a cheat code for getting a quick answer. I know the structure of a Wikipedia article, and if I want an answer to a simple question, like “where was E.B. Lewis born”, just finding it on Wikipedia is the fastest way to get it. But what about more complex questions? What if I wanted to know, say, who are all of the Black artists who were born in Philadelphia? For that, I can turn to Wikidata. Wikidata is an attempt to convert all of the world’s information into a structured, queryable dataset. As part of the 2021 LEADING Fellowship, the team worked a project enhancing and analyzing the available information on Philadelphia’s Black artists. While the project was initially focused on the artists themselves, during the research I became interested in the limitations and challenges of Wikidata as a platform. There are a number of issues surrounding the way that demographic tagging on Wikidata occurs. As a community resource, Wikidata editors preach extreme caution in applying ethnicity properties to Wikidata items. While some of the reasoning behind this is noble, it causes incompleteness in the catalog and hides how diverse the catalog really is. It also obscures whiteness, which is not represented on Wikidata almost at all. Through use of the Wikidata SPARQL endpoint and Python tools, jay was able to examine some of these challenges by analyzing the data Wikidata provided. This digital poster presentation will examine that process, as well as looking at what Wikidata editors and other libraries are already doing to attempt to mitigate some of these issues.