We introduce a multi-step reasoning framework using prompt-based LLMs to examine the relationship between social media language patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of gists of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists at-scale. Using RBIC, we systematically extract gists from subreddit discussions opposing COVID-19 health measures (Study 1). We then track how these gists evolve across key events (Study 2) and assess their influence on online engagement (Study 3). Finally, we investigate how the volume of gists is associated with national health trends like vaccine uptake and hospitalizations (Study 4). Our work is the first to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of prompt-based LLMs in identifying critical online discussion patterns that can form the basis of public health communication strategies.

翻译：我们提出了一种基于提示的大语言模型多步推理框架，用于考察社交媒体语言模式与全国健康结果趋势之间的关系。本研究以模糊痕迹理论为基础——该理论强调因果连贯性主旨（gists）在有效健康传播中的重要性——引入角色递增式提示（RBIC），一种基于提示的大语言模型框架，用于大规模识别主旨。通过RBIC，我们系统性地从反对COVID-19健康措施的Reddit子论坛讨论中提取主旨（研究1）。随后追踪这些主旨如何随关键事件演变（研究2），并评估其对在线参与的影响（研究3）。最后，我们探讨主旨数量与疫苗覆盖率、住院率等全国健康趋势的关联性（研究4）。本研究首次实证性地将社交媒体语言模式与现实世界公共卫生趋势相联系，揭示了基于提示的大语言模型在识别关键在线讨论模式方面的潜力，这些模式可作为公共卫生传播策略的基础。