使用大型语言模型自动化对健康内容的诱导性主题分析:使用社交媒体数据进行概念验证研究
JaMor Hairston1, Ritvik Ranjan2, Sahithi Lakamana1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, United States.
JAMIA open
|September 23, 2025
概括
大型语言模型 (LLM) 可以自动化对社交媒体数据的专题分析. 像GPT-4o这样的LLM的Few-shot提示显示了对定性研究的承诺,补充了专家编码.
科学领域:
- 计算语言学计算语言学
- 定性研究方法 定性研究方法
- 社交媒体分析
背景情况:
- 感应性主题分析需要大量的领域专业知识.
- 大型语言模型 (LLM) 在复制这种专家驱动的过程中面临着挑战.
- 对社交媒体数据进行主题分析的自动化是一个关键的研究目标.
研究的目的:
- 评估使用LLM用于专家驱动的社交媒体数据主题分析的可行性.
- 评估LLM在复制定性数据的人类编码方面的表现.
- 探索LLMs作为定性研究的可扩展补充的潜力.
主要方法:
- 利用Reddit上的两组数据集来进行模型优化和验证.
- 使用二进制分类对专家编码进行了评估,对五个LLM进行了评估.
- 采用零,单一和少数射击提示策略,通过准确性,精度,回忆和F1分数来衡量性能.
主要成果:
- 使用2次射击提示的GPT-4o实现了90.9%的准确性,并在验证集上获得了0.71 F1分.
- 模型衍生的主题分布与高流行性主题的专家分类密切匹配.
- 法学士课程的表现表明,复制专家定性分析的潜力很大.
结论:
- 基于简单的LLM方法可以有效地自动化主题分析.
- 课程提供了一个可扩展和有效的补充传统的定性研究方法.
- 本研究展示了LLM在分析社交媒体话语中的实际应用.
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