Quantifying gendered citation imbalance in computer science conferences

TL;DR

This study analyzes gender-based citation imbalances in computer science conferences, revealing strong homophily effects and highlighting disparities in top-tier venues, with implications for understanding research influence and network dynamics.

Contribution

Develops a novel family of citation network models and applies them to quantify gendered citation imbalances in computer science conference papers.

Findings

Gendered citation imbalance is strongly linked to citation homophily.

Imbalance is most significant in top-ranked conferences.

The imbalance persists across various subfields and affects citation rankings.

Abstract

The number of citations received by papers often exhibits imbalances in terms of author attributes such as country of affiliation and gender. While recent studies have quantified citation imbalance in terms of the authors' gender in journal papers, the computer science discipline, where researchers frequently present their work at conferences, may exhibit unique patterns in gendered citation imbalance. Additionally, understanding how network properties in citations influence citation imbalances remains challenging due to a lack of suitable reference models. In this paper, we develop a family of reference models for citation networks and investigate gender imbalance in citations between papers published in computer science conferences. By deploying these reference models, we found that homophily in citations is strongly associated with gendered citation imbalance in computer science, whereas heterogeneity in the number of citations received per paper has a relatively minor association with it. Furthermore, we found that the gendered citation imbalance is most pronounced in papers published in the highest-ranked conferences, is present across different subfields, and extends to citation-based rankings of papers. Our study provides a framework for investigating associations between network properties and citation imbalances, aiming to enhance our understanding of the structure and dynamics of citations between research publications.