Purpose: This study examines recent methodological trends in sociology, focusing on the increasing role of digitalization, computational methods, and interdisciplinary approaches. It aims to analyse how methodological shifts influence sociological research paradigms.
Method: A content analysis and text mining approach were applied to 391 articles published between 2021 and 2024 in five leading SSCI-indexed sociology journals. Using Maxqda and Julius AI, methodological trends were identified based on keyword analysis and classification of research techniques.
Findings: The analysis reveals a growing emphasis on computational sociology, big data analytics, and AI-assisted research methods. Traditional qualitative and mixed-method approaches remain significant, particularly in critical and comparative sociology. Digitalization has expanded methodological pluralism, yet concerns about algorithmic bias, data ethics, and Western-centric knowledge production persist.
Implications for Research and Practice: The study highlights the necessity of integrating computational methods into sociological education while preserving qualitative methodologies. Ethical considerations in big data research must be addressed, and sociology curricula should incorporate AI-based research techniques. Future studies should examine methodological transformations across broader disciplines and regions.
Keywords: Computational sociology, mixed methods, big data, digitalization, methodological shifts.