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.

我们使用基于提示的语言模型引入了一种多步推理框架，以检测社交媒体语言模式与国家健康状况趋势之间的关系，通过基于角色的增量辅导（RBIC）这种基于提示的语言模型框架，系统提取来自反对 COVID-19 健康措施的子论坛讨论中的要点，然后跟踪这些要点在关键事件中的演变，并评估其对在线参与的影响，最后调查要点数量与疫苗接种率和住院率等国家卫生趋势的关联，我们的工作是首次将社交媒体语言模式与现实世界的公共卫生趋势相联系，突出了基于提示的语言模型在识别形成公共卫生传播策略基础上的关键在线讨论模式的潜力。