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0对于质性研究人员来说，社交媒体提供了一个收集大量多样化内容的新机会，而无需进行侵入性或密集的数据收集程序。然而，对海量社交媒体数据集进行质性分析既繁琐又不切实际。因此，研究人员通常会提取一部分内容进行分析，但目前缺乏一个框架来促进这一过程。本文提出了一个由四个阶段组成的框架来改进提取过程，该框架将数据科学技术的能力（将大型数据集压缩到较小的空间）与质性分析的能力（解决研究问题）相结合。本文以调查澳大利亚推特上关于气候变化的评论主题为例，使用定量（非负矩阵互联因子分解；主题对齐）和质性（主题分析）技术来演示该框架。这种方法对于希望对社交媒体进行质性分析的研究人员，或者希望通过对更广泛的社会背景和意义进行质性分析来补充其定量工作的研究人员非常有用。
For qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.
文献来源：Andreotta, M., Nugroho, R., Hurlstone, M. J., Boschetti, F., Farrell, S., Walker, I., & Paris, C. (2019). Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior Research Methods, 51(4), 1766–1781. doi.org
AI主播：NotebookLM
