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

arXiv: https://arxiv.org/abs/2506.06083 · HTML: https://mlsanity.com/paper/2506.06083/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 98 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models](https://mlsanity.com/paper/2405.18638/) (2024)
- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2102.03702/) (2021)
- [A Query-Driven Topic Model](https://mlsanity.com/paper/2106.07346/) (2021)
- [Algorithmic failure as a humanities methodology: machine learning's mispredictions identify rich cases for qualitative analysis](https://mlsanity.com/paper/2305.11663/) (2023)
- [Analysis and tuning of hierarchical topic models based on Renyi entropy approach](https://mlsanity.com/paper/2101.07598/) (2021)
- [Using Computational Grounded Theory to Understand Tutors' Experiences in the Gig Economy](https://mlsanity.com/paper/2201.09787/) (2022)
- [Combining Topic Modeling with Grounded Theory: Case Studies of Project Collaboration](https://mlsanity.com/paper/2207.02212/) (2022)

## Built on by (most cited first, 2 of 2)

- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication](https://mlsanity.com/paper/2606.26541/) (2026)

## Abstract (arXiv)

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.