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

Gahl Silverman (), Dov Te’eni, David G. Schwartz, Yossi Mann, Daniel Cohen and Dafna Lewinsky
Additional contact information
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

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 4, No 8, 3137-3158

Abstract: 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.

Keywords: Qualitative enhancement; Reciprocal feedback loop mechanism; Sense-making; Augmented text classification (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02108-8

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