Voices of Her: Analyzing Gender Differences in the AI Publication World

TL;DR

This study analyzes gender differences in the AI research community, revealing disparities in citations, co-authorship patterns, and linguistic styles of papers, highlighting ongoing gender dynamics and promoting diversity.

Contribution

It provides a comprehensive analysis of gender differences across multiple aspects in the AI community using a large dataset, which was previously lacking.

Findings

Female researchers have fewer citations overall, but not across all academic ages.

Large gender homophily exists in co-authorship networks.

Female first-authored papers tend to have longer texts, positive emotion words, and catchy titles.

Abstract

While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends. Using the AI Scholar dataset of 78K researchers in the field of AI, we identify several gender differences: (1) Although female researchers tend to have fewer overall citations than males, this citation difference does not hold for all academic-age groups; (2) There exist large gender homophily in co-authorship on AI papers; (3) Female first-authored papers show distinct linguistic styles, such as longer text, more positive emotion words, and more catchy titles than male first-authored papers. Our analysis provides a window into the current demographic trends in our AI community, and encourages more gender equality and diversity in the future. Our code and data are at https://github.com/causalNLP/ai-scholar-gender.