Gender Trends in Computer Science Authorship

This article presents a large-scale automated analysis of gender trends in the authorship of Computer Science literature. We answer these questions by performing an automated study of literature metadata from scientific conferences and journals, using data from the Semantic Scholar academic search engine.a Our study incorporates metadata from 11.8M Computer Science publications. To provide a basis for comparison, we also analyze more than 140M articles from other fields of study. Our results demonstrate that although progress has been made, there is still a significant gap in gender representation among Computer Science authors. Continued delay in addressing the gender gap may perpetuate imbalances for generations to come. Our analysis was performed over the Semantic Scholar literature corpus.2 The corpus contains publications between 1940 and the end of November 2019, and associated metadata such as title, abstract, authors, publication venue, and year of publication.