将人机协作融入扎根理论，高效分析大规模社交数据。
A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
- 引入人机协同机制，让研究人员掌控分析流程
- 在Reddit数据上验证，成功解析零工经济下导师经历
- 兼顾计算效率与研究可信度，适合社会科学研究者
大数据的普及显著拓展了行为与社会科学的大规模研究可能性。在定性数据分析中，传统方法依赖大量人工，难以应用于大规模数据集。通过整合新兴计算方法可缓解可扩展性问题，但使用机器学习（ML）和自然语言处理（NLP）工具时，结果的可信度常受质疑。本文主张采用‘人机协同’方法，使研究者在保持对分析过程控制的同时，利用ML和NLP优势。为此，我们提出一种新型计算扎根理论（CGT）框架，支持大规模定性数据的分析，并延续经典扎根理论（GT）的方法严谨性。为验证其价值，我们在一项针对零工经济中导师经验的研究中，基于Reddit数据集测试该框架，取得有效成果。
原文摘要 · Abstract (English)
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.
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