Bibliometric Analysis of Gender Inequality Research Using Data Mining Techniques: Trends, Key Insights, and Future Directions

This bibliometric analysis study aims to explore the intersection of gender inequality and artificial intelligence research by uncovering key contemporary trends, influential works, and emerging themes in this interdisciplinary field. To achieve this, the study employed content and bibliometric analysis methods to examine 5,074 academic publications indexed in the Scopus database. These publications focus on gender inequality and AI-related areas such as data mining, machine learning, and predictive modeling. By using tools such as VOSviewer and Python-based analytical techniques, the study identified thematic trends, methodological approaches, and interdisciplinary patterns across various sectors including education, healthcare, and the workplace. The analysis also revealed significant gaps and evolving directions in the literature, offering a comprehensive view of how data-driven methods have been applied to understand and address gender inequality. By focusing on the most cited publications, prominent authors, and international collaborations, the analysis provides a comprehensive assessment of the current state of the field. Furthermore, by identifying thematic clusters and research gaps, the study sheds light on the evolving approaches to addressing gender inequality using modern data-driven methods. This research contributes to the growing body of literature that seeks to harness data science for social good and to promote a deeper understanding of gender-related challenges in contemporary societies. In addition, it addresses the relationship between gender theories and computational methodologies, particularly the intersection of gender perspectives with data mining and artificial intelligence.