A Comprehensive Review and Evaluation of Deep Learning Methods in Social Sciences
Copyright (c) 2024 Amir Mosavi, Sina Ardabili, Makó Csaba, Sasvári Péter
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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 the 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 the social sciences. Following PRISMA guideline, this study identifies fundamental DL methods applied to social science applications. We evaluated DL models using reported metrics and calculated a normalised reliability score for uniform assessment. Employing relief feature selection, we identified influential parameters affecting DL techniques’ reliability. Findings suggest that 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 the social sciences, emphasising the importance of explainability and responsibility.
Keywords:
References
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