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

Nesibe Manav Mutlu; Polathan Küsbeci

Makale detayları

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

Yazarlar:

Nesibe Manav Mutlu | Polathan Küsbeci

Yayıncı:

Anadolu University

Ana Başlıklar:

Sosyal Bilimlerde ve Eğitimde Bilgi İşleme | Veri Madenciliği ve Bilgi Keşfi | Eşitsizlik Sosyolojisi

Dergi:

Anadolu Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 3, pp.79-102

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358795

pISSN:

2667-8683

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 Bu bibliyometrik analiz çalışması, toplumsal cinsiyet eşitsizliği ile yapay zekâ araştırmalarının kesişim noktasını inceleyerek, bu disiplinler arası alandaki güncel eğilimleri, etkili çalışmaları ve ortaya çıkan temaları ortaya koymayı amaçlamaktadır. Bu doğrultuda, Scopus veri tabanında indekslenen, toplumsal cinsiyet eşitsizliği ve yapay zekâyla ilişkili alanlara veri madenciliği, makine öğrenmesi, kestirimsel modelleme vb. odaklanan 5.074 akademik yayın, içerik ve bibliyometrik analiz yöntemleriyle incelenmiştir. VOSviewer yazılımı ve Python tabanlı analiz araçları kullanılarak, eğitim, sağlık ve iş dünyası gibi çeşitli sektörlerde tematik eğilimler, yöntemsel yaklaşımlar ve disiplinlerarası örüntüler belirlenmiştir. Yapılan analizler, literatürdeki temel boşlukları ve gelişen araştırma yönelimlerini ortaya koyarak, veri odaklı yöntemlerin toplumsal cinsiyet eşitsizliğini anlama ve çözme sürecindeki rolüne dair kapsamlı bir çerçeve sunmaktadır. Analiz, en çok atıf alan makalelere, etkili yazarlara ve ülkesel iş birliklerine odaklanarak, alanın mevcut durumunun kapsamlı bir değerlendirmesini sunmaktadır. Dahası, tema kümelerini ve araştırma boşluklarını belirleyerek, modern veri odaklı yöntemleri kullanarak cinsiyet eşitsizliğiyle mücadele için geçmişten günümüze değişen yaklaşımlara ışık tutmaktadır. Bu çalışma, veri bilimini toplumsal fayda için kullanmayı amaçlayan ve modern toplumlarda cinsiyetle ilgili zorlukların daha derin bir şekilde anlaşılmasını teşvik eden büyüyen literatüre katkıda bulunmaktadır. Ayrıca, toplumsal cinsiyet kuramları ve veri madenciliği, yapay zeka arasındaki ilişkiyi ele almaktadır.

Makale detayları

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

Yazarlar:

Nesibe Manav Mutlu | Polathan Küsbeci

Yayıncı:

Anadolu University

Ana Başlıklar:

Sosyal Bilimlerde ve Eğitimde Bilgi İşleme | Veri Madenciliği ve Bilgi Keşfi | Eşitsizlik Sosyolojisi

Dergi:

Anadolu Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 3, pp.79-102

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358795

pISSN:

2667-8683

Bibliometric analysis

gender inequality

artificial intelligence

data miningBibliyometrik analiz

cinsiyet eşitsizliği

yapay zeka

veri madenciliği

Sayı

Makale detayları

Yazarlar: Nesibe Manav Mutlu | Polathan Küsbeci

Yayıncı: Anadolu University

Ana Başlıklar: Sosyal Bilimlerde ve Eğitimde Bilgi İşleme | Veri Madenciliği ve Bilgi Keşfi | Eşitsizlik Sosyolojisi

Dergi: Anadolu Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 3, pp.79-102

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001358795

pISSN: 2667-8683

Sayı

Yazarlar:

Nesibe Manav Mutlu | Polathan Küsbeci

Yayıncı:

Anadolu University

Ana Başlıklar:

Sosyal Bilimlerde ve Eğitimde Bilgi İşleme | Veri Madenciliği ve Bilgi Keşfi | Eşitsizlik Sosyolojisi

Dergi:

Anadolu Üniversitesi Sosyal Bilimler Dergisi Vol.25 Issue 3, pp.79-102

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358795

pISSN:

2667-8683