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Machine learning, artificial neural networks and social research

Author

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  • Giovanni Di Franco

    (Sapienza University of Rome)

  • Michele Santurro

    (Sapienza University of Rome)

Abstract

Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different disciplines such as mathematics, statistics, computer science and neuroscience. This approach is characterized by the use of algorithms to extract knowledge from large and heterogeneous data sets. In addition to offering a brief introduction to ANN algorithms-based ML, in this paper we will focus our attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures. In this regard, we will provide three examples of applications on sociological data to assess the impact of ML in the study of relationships between variables. Finally, we will compare the potential of ML with traditional data analysis models.

Suggested Citation

  • Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:3:d:10.1007_s11135-020-01037-y
    DOI: 10.1007/s11135-020-01037-y
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    References listed on IDEAS

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    1. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
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    Cited by:

    1. Hüseyin Cüce & Duygu Özçelik, 2022. "Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    2. Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.
    3. Mohamed-Amine Babay & Mustapha Adar & Ahmed Chebak & Mustapha Mabrouki, 2023. "Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning," Energies, MDPI, vol. 16(14), pages 1-21, July.
    4. Wadim Strielkowski & Svetlana Zenchenko & Anna Tarasova & Yana Radyukova, 2022. "Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications," Sustainability, MDPI, vol. 14(12), pages 1-17, June.

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