A novel, human-in-the-loop computational grounded theory framework for big social data

• Published in: Big data & society Vol. 12; no. 2
• Main Authors: Alqazlan, Lama, Fang, Zheng, Castelle, Michael, Procter, Rob
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2025; Sage Publications Ltd; SAGE Publishing
• Subjects: Big Data; Credibility; Data analysis; Datasets; Gig economy; Grounded theory; Language acquisition; Machine learning; Natural language processing; Physical work; Qualitative analysis; Qualitative research; Social research; Social sciences; Computational grounded theory; human-in-the-loop natural language processing; big social data; query-driven topic modelling
• ISSN: 2053-9517; 2053-9517
• DOI: 10.1177/20539517251347598

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 and natural language processing 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 machine learning and natural language processing. With this in mind, we propose a novel methodological framework for computational grounded theory that supports the analysis of large qualitative datasets, while maintaining the rigour of established grounded theory 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.