Automatic gender detection: a methodological procedure and recommendations to computationally infer the gender from names with ChatGPT and gender APIs
Manuel Goyanes, Luis de‐Marcos, Adrián Domínguez‐Díaz
Abstract
Abstract Both computational social scientists and scientometric scholars alike, interested in gender-related research questions, need to classify the gender of observations. However, in most public and private databases, this information is typically unavailable, making it difficult to design studies aimed at understanding the role of gender in influencing citizens’ perceptions, attitudes, and behaviors. Against this backdrop, it is essential to design methodological procedures to infer the gender automatically and computationally from data already provided, thus facilitating the exploration and examination of gender-related research questions or hypotheses. Researchers can use automatic gender detection tools like Namsor or Gender-API, which are already on the market. However, recent developments in conversational bots offer a new, still relatively underexplored, alternative. This study offers a step-by-step research guide, with relevant examples and detailed clarifications, to automatically classify the gender from names through ChatGPT and two partially free gender detection tool (Namsor and Gender-API). In addition, the study provides methodological suggestions and recommendations on how to gather, interpret, and report results coming from both platforms. The study methodologically contributes to the scientometric literature by describing an easy-to-execute methodological procedure that enables the computational codification of gender from names. This procedure could be implemented by scholars without advanced computing skills.