Gender Disparities in StackOverflow's Community-Based Question Answering: A Matter of Quantity versus Quality

TL;DR

This work investigates gender disparities in Stack Overflow by separating perceived gender effects from actual answer quality. It fuses human validation, feature-based statistics, and LLM-driven rankings to assess whether gender influences answer quality or the selection of the accepted answer. The study finds no meaningful quality gap between male and female respondents, while disparities in reputation mainly reflect activity levels and a reputation system biased toward high-volume contributions. The findings highlight the need for more nuanced reputation designs that reward diverse, merit-based contributions to promote a more inclusive CQA ecosystem with practical implications for platform design.

Abstract

Community Question-Answering platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess the quality of answers and participants' expertise, ideally free from discriminatory biases. However, prior research has highlighted persistent gender biases, raising concerns about the inclusivity and fairness of these systems. Addressing such biases is crucial for fostering equitable online communities. While previous studies focus on detecting gender bias by comparing male and female user characteristics, they often overlook the interaction between genders, inherent answer quality, and the selection of ``best answers'' by question askers. In this study, we investigate whether answer quality is influenced by gender using a combination of human evaluations and automated assessments powered by Large Language Models. Our findings reveal no significant gender differences in answer quality, nor any substantial influence of gender bias on the selection of ``best answers." Instead, we find that the significant gender disparities in SO's reputation scores are primarily attributable to differences in users' activity levels, e.g., the number of questions and answers they write. Our results have important implications for the design of scoring systems in community question-answering platforms. In particular, reputation systems that heavily emphasize activity volume risk amplifying gender disparities that do not reflect actual differences in answer quality, calling for more equitable design strategies.