Theme-driven Keyphrase Extraction to Analyze Social Media Discourse

TL;DR

This paper presents a novel theme-driven keyphrase extraction framework tailored for social media health content, demonstrating its effectiveness in extracting clinically relevant keyphrases, especially for opioid use disorder discussions, with large language models outperforming traditional methods.

Contribution

Introduces a new theme-driven keyphrase extraction approach, a curated dataset from Reddit, and evaluates the performance of large language models like ChatGPT for social media health text analysis.

Findings

Large language model (ChatGPT) outperforms unsupervised models in keyphrase extraction.

Developed the first dataset of human-annotated health keyphrases from social media.

Proved the feasibility of minimally supervised NLP models for extracting actionable health insights.

Abstract

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.