A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data

TL;DR

This paper introduces a human-in-the-loop computational grounded theory framework designed for analyzing large social datasets, combining qualitative rigor with scalable machine learning and NLP tools.

Contribution

It presents a novel methodological framework that integrates human oversight with computational methods for large-scale qualitative data analysis.

Findings

Framework effectively analyzes large Reddit datasets.

Maintains qualitative rigor with computational efficiency.

Provides researchers control over ML/NLP analysis processes.

Abstract

The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical concern for researchers is the trustworthiness of results when Machine Learning (ML) and Natural Language Processing (NLP) tools are used to analyse such data. We argue that confidence in the credibility and robustness of results depends on adopting a 'human-in-the-loop' methodology that is able to provide researchers with control over the analytical process, while retaining the benefits of using ML and NLP. With this in mind, we propose a novel methodological framework for Computational Grounded Theory (CGT) that supports the analysis of large qualitative datasets, while maintaining the rigour of established Grounded Theory (GT) methodologies. To illustrate the framework's value, we present the results of testing it on a dataset collected from Reddit in a study aimed at understanding tutors' experiences in the gig economy.