# Characterizing Information Needs and Peer Engagement Regarding Medication for Opioid Use Disorder on Social Media

> **NIH NIH R21** · DARTMOUTH COLLEGE · 2024 · $451,000

## Abstract

PROJECT SUMMARY: The opioid crisis has devastating impacts, resulting in $35 billion in healthcare costs,
$92 billion in lost productivity, and over 100,000 deaths annually. Effective treatment with Medications for
Opioid Use Disorder (MOUD) like methadone or buprenorphine can significantly reduce opioid-related
mortality. However, individuals with Opioid Use Disorder (OUD) face critical unmet information needs about
MOUD treatment, often turning to social media due to stigma, lack of trust, and resources.
This project aims to leverage Natural Language Processing (NLP) and mixed methodologies to identify and
classify medication treatment information needs of individuals with OUD on Reddit, a popular social media
platform. The goal is to extract clinically relevant insights to improve MOUD treatment access and quality.
Three specific aims guide this project:
AIM 1: Identify buprenorphine- and methadone-related TINs self-reported by persons with expressed opioid
use disorder on Reddit. A large dataset of relevant Reddit posts will be curated, and a qualitative coding
protocol developed to systematically identify MOUD treatment information needs.
AIM 2: Evaluate feasibility of reliably classifying buprenorphine and methadone-related TINS on Reddit using
state-of-the-art NLP methods to enable efficient extraction of MOUD TINs on social media. Success will be
defined by achieving an F1 score of 80% or higher and statistically significant improvements over standard
baseline models.
AIM 3: Characterize the nature of peer engagement on Reddit to identify areas of MOUD misinformation and
information gaps, promoted self-treatment strategies, and stigma. Both quantitative and qualitative methods
will be employed to achieve this aim.
The impact of this project is significant, as it will produce new, validated methods for efficiently extracting
actionable insights from social media data. The outcomes align with priorities outlined by the National Institute
on Drug Abuse (NIDA), including leveraging data science to understand real-world complexity and developing
personalized interventions informed by people with lived experience. This work will establish a foundation for
future proposals aimed at advancing computational health in the context of substance use disorders.

## Key facts

- **NIH application ID:** 11058063
- **Project number:** 1R21DA059665-01A1
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Sarah Masud Preum
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $451,000
- **Award type:** 1
- **Project period:** 2024-09-30 → 2026-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11058063

## Citation

> US National Institutes of Health, RePORTER application 11058063, Characterizing Information Needs and Peer Engagement Regarding Medication for Opioid Use Disorder on Social Media (1R21DA059665-01A1). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/11058063. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*