Gender Disparities in StackOverflow's Community-Based Question Answering: A Matter of Quantity versus Quality
Maddalena Amendola, Cosimo Rulli, Carlos Castillo, Andrea Passarella, Raffaele Perego

TL;DR
This study examines gender disparities in Stack Overflow, finding that differences in reputation scores are mainly due to activity levels rather than answer quality, highlighting the need for fairer scoring mechanisms.
Contribution
It uniquely combines human and AI evaluations to assess answer quality and reveals that gender bias does not significantly affect answer quality or selection, but activity levels do.
Findings
No significant gender difference in answer quality.
Gender disparities in reputation are driven by activity levels.
Reputation systems emphasizing activity may worsen gender inequality.
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…
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Taxonomy
TopicsExpert finding and Q&A systems · Information Retrieval and Search Behavior · Topic Modeling
