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

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Ding, Xiaohan, Carik, Buse, Gunturi, Uma Sushmitha, Reyna, Valerie, Rho, Eugenia H.
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Human-Computer Interaction Artificial Intelligence Computation and Language Social and Information Networks
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

_version_	1866910350451933184
author	Ding, Xiaohan Carik, Buse Gunturi, Uma Sushmitha Reyna, Valerie Rho, Eugenia H.
author_facet	Ding, Xiaohan Carik, Buse Gunturi, Uma Sushmitha Reyna, Valerie Rho, Eugenia H.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_00994
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language Ding, Xiaohan Carik, Buse Gunturi, Uma Sushmitha Reyna, Valerie Rho, Eugenia H. Human-Computer Interaction Artificial Intelligence Computation and Language Social and Information Networks 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.
title	Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
topic	Human-Computer Interaction Artificial Intelligence Computation and Language Social and Information Networks
url	https://arxiv.org/abs/2403.00994