Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit

TL;DR

This work tackles the public health need to understand what questions people with opioid use disorder ask online. It introduces a transformer-based question detector and a hierarchically-clustered, topic-modeling framework applied to 204,559 Reddit posts from 19 OUD-related subreddits spanning 2018–2021. The study yields 69 fine-grained topics organized into 10 macro groups, with dark web drug procurement and Suboxone dosing among the most prominent themes, and demonstrates high expert agreement in topic labeling. The results inform public health interventions, moderation strategies, and harm-reduction messaging, while acknowledging limitations such as text-only data and platform specificity.

Abstract

Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reasons, including the stigma faced by people using opioids, online communities for recovery and support were formed on different social media platforms. In these communities, people share their experiences and solicit information by asking questions to learn about opioid use and recovery. However, these communities do not always contain clinically verified information. In this paper, we study natural language questions asked in the context of OUD-related discourse on Reddit. We adopt transformer-based question detection along with hierarchical clustering across 19 subreddits to identify six coarse-grained categories and 69 fine-grained categories of OUD-related questions. Our analysis uncovers ten areas of information seeking from Reddit users in the context of OUD: drug sales, specific drug-related questions, OUD treatment, drug uses, side effects, withdrawal, lifestyle, drug testing, pain management and others, during the study period of 2018-2021. Our work provides a major step in improving the understanding of OUD-related questions people ask unobtrusively on Reddit. We finally discuss technological interventions and public health harm reduction techniques based on the topics of these questions.

Figures (4)

• Figure 1: Data Processing Pipeline. We first preprocess Reddit posts by normalizing text and correcting errors, then using a deep neural network to detect and filter questions. Then, we cluster the model generated summary of these posts into meaningful topics using hierarchical clustering.
• Figure 2: Topics categorized into groups by public health experts based on the top 100 relevant posts in every topic. Drug sales accounts for the highest volume by topic groups (a) which is also evident in the top 20 topics (b) where topics of the dark web and vendors have the highest counts.
• Figure 3: Relative position of question in a post on average. Most of the posts have questions asked at the end of post.
• Figure 4: We ran HDBScan for a range of minimum of clusters sizes as shown in Figure (a). The maximum mean topic coherance (UMASS) corresponds to a min cluster size of 200. In Figure (b), that is equivalent to 69 clusters (or topics).