Leveraging Prompt-Based Large Language Models: Predicting Pandemic
  Health Decisions and Outcomes Through Social Media Language
Xiaohan Ding Buse Carik Uma Sushmitha Gunturi Valerie Reyna Eugenia H. Rho
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.