Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
This work addresses public health communication challenges by empirically connecting online linguistic patterns to actual health outcomes, offering a novel approach for informing public health strategies.
The researchers tackled the problem of linking social media language patterns to real-world health outcomes by developing a prompt-based LLM framework called Role-Based Incremental Coaching (RBIC) to extract 'gists' from discussions opposing COVID-19 measures, finding that the volume of these gists was associated with national trends in vaccine uptake and hospitalizations.
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.