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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

TL;DR

This 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.

Abstract

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.

Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language

TL;DR

This 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.

Abstract

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.
Paper Structure (36 sections, 3 figures, 9 tables, 1 algorithm)

This paper contains 36 sections, 3 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Understanding the Impact of Social Media Language Patterns on Health Decisions and Outcomes
  4. Understanding Health Discourse Through the Lens of Fuzzy-Trace Theory and Its Core Concept of Gist
  5. Challenges in Predicting Semantic Causality in Online Health Discourse
  6. Study 1: Predicting Gists in Social Media Conversations at-Scale
  7. Data
  8. Method: Role-Based Incremental Coaching (RBIC)
  9. Human Evaluation.
  10. Result
  11. Study 2: How Gists Evolve Over Time
  12. Method
  13. Extracting Sentence Embeddings from Gists.
  14. Clustering of Sentence Embeddings.
  15. Verifying Gist Clusters.
  16. ...and 21 more sections

Figures (3)

  • Figure 1: Illustration of the Role-Based Incremental Coaching (RBIC) prompting framework: RBIC incorporates role-based cognition and sub-task training to improve the model's comprehension of a specific task before generating the final output.
  • Figure 2: The upper portion of the illustration displays the progression of clusters across four-month periods. The line graph illustrates the month-by-month evolution of the number of posts containing gists, representing the central themes discussed on Reddit concerning health mandates. The graph highlights specific dates when each topic was most prominently discussed and presents relevant news events related to COVID-19 and health mandates during those periods.
  • Figure 3: Design Implication: Illustration of moderator’s view of a submitted post or comment.