Using Bibliometric Analysis to Measure and Understand the Gender Gap in Published Computing Books: Gender Gap in Computer Science

Using Bibliometric Analysis to Measure and Understand the Gender Gap in Published Computing Books: Gender Gap in Computer Science

Arbana Kadriu (SEE University, Macedonia), Kosovare Sahatqija (SEE University, Macedonia) and Lejla Abazi-Bexheti (SEE University, Macedonia)
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJESMA.2021010103
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Abstract

The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles.
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The field of social network analysis has received a lot of interest from different authors, with a crucial role in analyzing and at the same time forecasting future relations. Several authors investigated this topic by analyzing books’ collaboration networks.

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