  In recent years, the United States has witnessed a significant surge in the
popularity of vaping or e-cigarette use, leading to a notable rise in cases of
e-cigarette and vaping use-associated lung injury (EVALI) that caused
hospitalizations and fatalities during the EVALI outbreak in 2019, highlighting
the urgency to comprehend vaping behaviors and develop effective strategies for
cessation. Due to the ubiquity of social media platforms, over 4.7 billion
users worldwide use them for connectivity, communications, news, and
entertainment with a significant portion of the discourse related to health,
thereby establishing social media data as an invaluable organic data resource
for public health research. In this study, we extracted a sample dataset from
one vaping sub-community on Reddit to analyze users' quit-vaping intentions.
Leveraging OpenAI's latest large language model GPT-4 for sentence-level quit
vaping intention detection, this study compares the outcomes of this model
against layman and clinical expert annotations. Using different prompting
strategies such as zero-shot, one-shot, few-shot and chain-of-thought
prompting, we developed 8 prompts with varying levels of detail to explain the
task to GPT-4 and also evaluated the performance of the strategies against each
other. These preliminary findings emphasize the potential of GPT-4 in social
media data analysis, especially in identifying users' subtle intentions that
may elude human detection.
