A Comprehensive Review and Evaluation of Deep Learning Methods in Social Sciences

Abstract

Artificial intelligence (AI) is widely used in social sciences and continues to evolve. Deep learning (DL) has emerged as a powerful AI tool transforming social sciences with valuable insights across many areas. Employing DL for modelling social sciences’ big data has led to significant discoveries and transformations. This study aims to systematically review and evaluate DL methods in social sciences. Following PRISMA guideline, this study identifies fundamntal DL methods applied to social science applications. We evaluated DL models using reported metrics and calculated a normalized reliability score for uniform assessment. Employing relief feature selection, we identified influential parameters affecting DL techniques’ reliability. Findings suggest evaluation criteria significantly impact DL model effectiveness, while database and application type influence moderately. Identified limitations include inadequate reporting of evaluation criteria and model structure details hindering comprehensive assessment and informed policy development. In conclusion, this review underscores DL methods’ transformative role in social sciences, emphasising the importance of explainability and responsibility.