Author ORCID Identifier

https://orcid.org/0009-0001-1206-105X

Date of Award

Spring 6-5-2026

Document Type

Thesis (Undergraduate)

Department

Computer Science

First Advisor

Dr. Sarah M. Preum

Abstract

Health online discussion boards are a modern platform that allow patients to interact with each other and the healthcare system as a whole, making them valuable sources of information for clinicians seeking to better anticipate treatment experiences and barriers. This study focuses on one such community, r/suboxone, a subreddit where patients using Suboxone share their experiences and ask questions. Our analysis is motivated by previous work that proposes event-based classification systems for such posts which buckets posts from r/suboxone into one or more of five high-level labels (Access Logistics, Co-Occurring Drug Usage, Medication for Opioid Use Disorder Administration, Psychophysical Effects, and Tapering). We expand upon this work by proposing a clustering and topic extraction pipeline to group documents and extract key sub-themes from such groupings. To address difficulties in natural language processing of social media data, we explore the utility of varying post representations by leveraging Large Language Model summarization and keyphrase extraction using neural embeddings as proxies for raw posts. We then explore the utility of Large Language models in summarizing the resultant clusters' key themes. We observe moderate to strong clustering results that are consistent across the three post representations (DBCV = 0.50-0.67), with no one post-representation producing consistently stronger clustering results. We find that manual assessment of such clusters results in varying perceived homogeneity, with some containing highly coherent and relevant topics, while other clusters deemed to be less consistent and meaningful. Finally, we observe promising alignment between LLM-generated cluster summaries and manual evaluation.

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