Online data are constantly growing, providing a wide range of opportunities to explore social phenomena. Large Language Models (LLMs) capture the inherent structure, contextual meaning, and nuance of human language and are the base for state-of-the-art Natural Language Processing (NLP) algorithms. In this article, we describe a method to assist qualitative researchers in the theorization process by efficiently exploring and selecting the most relevant information from a large online dataset. Using LLM-based NLP algorithms, qualitative researchers can efficiently analyze large amounts of online data while still maintaining deep contact with the data and preserving the richness of qualitative analysis. We illustrate the usefulness of our method by examining 5,516 social media posts from 18 entrepreneurs pursuing an environmental mission (ecopreneurs) to analyze their impression management tactics. By helping researchers to explore and select online data efficiently, our method enhances their analytical capabilities, leads to new insights, and ensures precision in counting and classification, thus strengthening the theorization process. We argue that LLMs push researchers to rethink research methods as the distinction between qualitative and quantitative approaches becomes blurred.

中文翻译：

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使用人工智能增强理论化：利用大型语言模型对在线数据进行定性分析

在线数据不断增长，为探索社会现象提供了广泛的机会。大型语言模型 （LLM） 捕获人类语言的固有结构、上下文含义和细微差别，是最先进的自然语言处理 （NLP） 算法的基础。在本文中，我们描述了一种通过有效地从大型在线数据集中探索和选择最相关的信息来帮助定性研究人员进行理论化过程的方法。使用基于 LLM 的 NLP 算法，定性研究人员可以有效地分析大量在线数据，同时仍然保持与数据的深度联系并保留定性分析的丰富性。我们通过检查来自 18 位追求环境使命的企业家（生态企业家）的 5,516 篇社交媒体帖子来分析他们的印象管理策略，从而说明了我们方法的有用性。通过帮助研究人员有效地探索和选择在线数据，我们的方法增强了他们的分析能力，带来了新的见解，并确保了计数和分类的精确性，从而加强了理论化过程。我们认为，随着定性和定量方法之间的区别变得模糊，LLM 促使研究人员重新思考研究方法。