Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations

By Saif M. Mohammad. 2020

49 citations recorded by the source corpus.

Disparities in authorship and citations across genders can have substantial adverse consequences not just on the disadvantaged gender, but also on the field of study as a whole. In this work, we examine female first author percentages and the citations to their papers in Natural Language Processing. We find that only about 29% of first authors are female and only about 25% of last authors are female. Notably, this percentage has not improved since the mid 2000s. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience and area of research. We hope that recording citation and participation gaps across demographic groups will improve awareness of gender gaps and encourage more inclusiveness and fairness in research.

Subfields: General Social Sciences, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Social Sciences

1. Will E. Hipson
2. Saif M. Mohammad
3. February Friday

In Statistics and Probability

1. Will E. Hipson
2. Saif M. Mohammad

Citations in the corpus

• Examining Citations of Natural Language Processing Literature (2020, methodology): “(Further details about the dataset, as well as an analysis of the volume of research in NLP over the years, are available in Mohammad (2020b).)”
• NRC Publications Archive Archives des publications du CNRC (unknown): “…etc.)? how well cited are papers published in different time spans? how well cited are papers from different areas of research within NLP etc. Mohammad (2020b) quantifies and examines gender gap in Natural Language Processing Research; specifically, the disparities in authorship and citations…”
• PoKi: A Large Dataset of Poems by Children (2020, methodology): “Mohammad (2020) evaluated the above method on over 6,000 authors of Natural Language Processing papers, where the ground truth gender information was available.”