从数字痕迹到公共疫苗接种行为:利用大型语言模型进行大数据分类
Yoo Jung Oh1, Muhammad Ehab Rasul2, Emily McKinley2
1Department of Communication, Michigan State University, East Lansing, MI, United States.
Frontiers in artificial intelligence
|August 7, 2025
概括
大型语言模型 (LLM) 从社交媒体上有效捕捉COVID-19疫苗行为. 精心调整的GPT-4o-mini显示出卓越的分类准确性,将社交媒体趋势与实际疫苗接种率相关联.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 自然语言处理自然语言处理.
背景情况:
- 社交媒体平台是公共卫生信息和行为数据的丰富来源.
- 了解健康行为,如COVID-19疫苗接种,对于公共卫生干预至关重要.
- 大型语言模型 (LLM) 提供了分析非结构化文本数据的高级功能.
研究的目的:
- 评估LLM在从社交媒体捕捉COVID-19疫苗相关健康行为方面的有效性.
- 为了比较不同LLM模型 (GPT-4o,GPT-4o-mini,微调的GPT-4o-mini) 在分类疫苗接种行为的性能.
- 根据国家疫苗接种统计数据验证LLM衍生分类.
主要方法:
- 利用了2020-2021年的社交媒体帖子,专注于COVID-19疫苗.
- 采用快速工程和微调技术,使用最先进的法学士.
- 基于疫苗接种行为,意图和信息共享的机密帖子.
- 交叉验证的LLM分类与全国疫苗接种数据.
主要成果:
- 与GPT-4o和标准GPT-4o-mini相比,微调的GPT-4o-mini实现了更高的准确性,精度,回忆和F1得分.
- 分析显示,9.84%的帖子表明了个人接种疫苗的行为,71.45%涉及信息共享.
- 在社交媒体表达的疫苗接种行为和实际接种疫苗之间观察到强烈的正相关性 (r=0.76).
结论:
- 从社交媒体数据来估计现实世界的健康行为,LLM是有效的工具.
- 微调的LLM显著提高了他们在健康行为分类中的表现.
- 这种方法为公共卫生研究和战略制定提供了宝贵的见解.
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