# Online Evidence of Withdrawal Self-Medication

> **NIH NIH R21** · RESEARCH TRIANGLE INSTITUTE · 2020 · $268,226

## Abstract

PROJECT SUMMARY/ABSTRACT
Withdrawal symptoms from opioid use can be severe and are major contributing factors to relapse and
continuing misuse. Many opioid users are actively experimenting with “remedies” that can alleviate
withdrawal, and they are discussing their effectiveness in blogs and forums. In this pilot study we will use
Natural Language Processing (NLP) and human expertise to examine over 50,000 recent posts in two
Reddit forums OpiatesRecovery and Opiates to assess systematically which remedies are being used, how
they are being used, and what are the reported consequences of such self-help experimentation. We will
create a curated database of user-reported “remedies.” Information will be semiautomatically extracted from
the online, self-reported use of alternative treatments (i.e., other prescription drugs, over the counter
medications, food supplements, activities such as meditation and yoga). A team of a pharmacologist,
physician, and ethnographer will evaluate database entries to uncover (1) potential harm associated with
uncontrolled and unsupervised experimentation, (2) potentially effective available treatments (e.g., traditional
medicine), (3) potentially promising compound leads, and (4) patients' needs and issues that are most
important to them.
Aim 1. To assemble an extensive database of opioid withdrawal and remedy-associated terminology from
posts on OpiatesRecovery and Opiates Reddit communities. NLP will be used to build a language model that
understands how words are used in context (word2vec).
Aim 2. To develop a dataset of instances of self-reported remedy use from Reddit and conduct a bipartite
network analysis of remedies and users. Using NLP tools and the word embedding model we will develop an
exclusive dataset containing extracted information associated with remedies targeting withdrawal and
craving. This aim will use elements of artificial intelligence and close human supervision to extract remedies,
including variations of spelling, from the texts. The result of this aim will be a remedy database that includes
spelling variations and slang references and a network analysis linking remedies and users.
Aim 3. To organize, aggregate, and systematically assess information from mentions of remedy use.
Potential compounds and other remedies will be classified to provide an initial assessment of their potential
relevance to the opioid treatment process. This process will require the most human oversight and
assessment. Network analysis tools will be used to assess and identify the relationships between the types
of remedies and potential therapeutic effect and will create the benchmarks for similar future studies.

## Key facts

- **NIH application ID:** 9979829
- **Project number:** 5R21DA048739-02
- **Recipient organization:** RESEARCH TRIANGLE INSTITUTE
- **Principal Investigator:** GEORGIY BOBASHEV
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $268,226
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979829

## Citation

> US National Institutes of Health, RePORTER application 9979829, Online Evidence of Withdrawal Self-Medication (5R21DA048739-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9979829. Licensed CC0.

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