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A hybrid mixed methods design of qualitative enhancement and reciprocal feedback loop for augmented text classification

Author

Listed:
  • Gahl Silverman

    (Tel Aviv University)

  • Dov Te’eni

    (Tel Aviv University)

  • David G. Schwartz

    (Bar-Ilan University)

  • Yossi Mann

    (Bar-Ilan University)

  • Daniel Cohen

    (Bar-Ilan University)

  • Dafna Lewinsky

    (Bar-Ilan University)

Abstract

Keeping the ‘human-in-the-loop’ in automated text classification can improve its inference quality by supporting human sense-making that goes beyond current machine-learning algorithms. Hence, this methodological article presents a novel mixed-methods design that aims to enhance human sense-making and improve the inference quality of augmented text classification. It is a three-phase hybrid model: a preliminary qualitative phase, a core quantitative phase (i.e., the automated text classification), and a reciprocal feedback loop of a follow-up quantitative evaluation phase. This Hybrid mixed-methods design with a Reciprocal Feedback Loop is specified and then illustrated with a study of automated classification of illicit drug transaction messages in a Darknet forum. The article also discusses the conditions under which this design can improve the inference quality, and the benefit of reciprocal human–machine learning.

Suggested Citation

  • Gahl Silverman & Dov Te’eni & David G. Schwartz & Yossi Mann & Daniel Cohen & Dafna Lewinsky, 2025. "A hybrid mixed methods design of qualitative enhancement and reciprocal feedback loop for augmented text classification," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(4), pages 3137-3158, August.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02108-8
    DOI: 10.1007/s11135-025-02108-8
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    References listed on IDEAS

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    1. Dafna Lewinsky & Dov Te’eni & Inbal Yahav-Shenberger & David G. Schwartz & Gahl Silverman & Yossi Mann, 2024. "Detecting terrorist influencers using reciprocal human-machine learning: The case of militant Jihadist Da’wa on the Darknet," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    2. Julian Ashwin & Aditya Chhabra & Vijayendra Rao, 2023. "Using Large Language Models for Qualitative Analysis can Introduce Serious Bias," Papers 2309.17147, arXiv.org, revised Oct 2023.
    3. Bilgram, Volker & Gluth, Oliver & Piller, Frank T., 2017. "Crowdfunding Data as a Source of Innovation," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 34(3), pages 10-18.
    4. Karl E. Weick & Kathleen M. Sutcliffe & David Obstfeld, 2005. "Organizing and the Process of Sensemaking," Organization Science, INFORMS, vol. 16(4), pages 409-421, August.
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