  Social media platforms are vital resources for sharing self-reported health
experiences, offering rich data on various health topics. Despite advancements
in Natural Language Processing (NLP) enabling large-scale social media data
analysis, a gap remains in applying keyphrase extraction to health-related
content. Keyphrase extraction is used to identify salient concepts in social
media discourse without being constrained by predefined entity classes. This
paper introduces a theme-driven keyphrase extraction framework tailored for
social media, a pioneering approach designed to capture clinically relevant
keyphrases from user-generated health texts. Themes are defined as broad
categories determined by the objectives of the extraction task. We formulate
this novel task of theme-driven keyphrase extraction and demonstrate its
potential for efficiently mining social media text for the use case of
treatment for opioid use disorder. This paper leverages qualitative and
quantitative analysis to demonstrate the feasibility of extracting actionable
insights from social media data and efficiently extracting keyphrases using
minimally supervised NLP models. Our contributions include the development of a
novel data collection and curation framework for theme-driven keyphrase
extraction and the creation of MOUD-Keyphrase, the first dataset of its kind
comprising human-annotated keyphrases from a Reddit community. We also identify
the scope of minimally supervised NLP models to extract keyphrases from social
media data efficiently. Lastly, we found that a large language model (ChatGPT)
outperforms unsupervised keyphrase extraction models, and we evaluate its
efficacy in this task.
