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

Abstract

The availability of big social data has had a substantial influence on the possibilities and methodological choices for conducting large-scale social science research. Within the context of qualitative data analysis, the challenge lies in the fact that conventional methods such as grounded theory and thematic analysis require intensive manual labour and are often impossible to apply to large datasets. An effective approach to addressing this challenge involves incorporating emerging computational methods, including machine learning and natural language processing, to enhance the analytical capabilities and thus obtain precise and meaningful insights from the data. Nonetheless, it remains vital to maintain the robustness of conventional methods, which typically entail a thorough examination of the data at hand.

Therefore, this thesis proposes a novel methodological framework for applying computational grounded theory that supports the analysis of large qualitative datasets while maintaining the rigour of the established grounded theory methodology. The framework also takes into account the trustworthiness of results when machine learning and natural language processing models are used to construct new social theories. We argue that confidence in the credibility and robustness of results necessitates the adoption of a ‘human-in-the-loop’ approach to ensure quality and provide researchers with control over the analytical process.

To illustrate the value of the framework and its application in practice, this the- sis provides a case study aimed at understanding the working experiences of tutors in the gig economy. The results obtained from testing the framework on a dataset collected from Reddit are presented. This study’s focus on an understudied group makes it an important contribution to research into the gig economy while also demonstrating the potential of the proposed novel approach to computational grounded theory.