ArticleComputers in biology and medicine2021
Utilizing a multi-class classification approach to detect therapeutic and recreational misuse of opioids on Twitter.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Multinational appraisal of the epidemiological distribution of opioid fatalities: a systematic review and meta-analysis.Frontiers in psychiatry · 2023Pooled it
- Reasoning, Contrastive, and In-Context Strategies for Opioid Use Stage Detection on Social Media.Proceedings of the ... Workshop on Patient-Oriented Language Processing · 2026Article
- Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis.JMIR infodemiology · 2025Article
- Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis.JMIR infodemiology · 2025Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities.Complex psychiatry · 2022Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundOpioid misuse (OM) is a major health problem in the United States, and can lead to addiction and fatal overdose. We sought to employ natural language processing (NLP) and machine learning to categorize Twitter chatter based on the motive of OM. MATERIALS AND
methodsWe collected data from Twitter using opioid-related keywords, and manually annotated 6988 tweets into three classes-No-OM, Pain-related-OM, and Recreational-OM-with the No-OM class representing tweets indicating no use/misuse, and the Pain-related misuse and Recreational-misuse classes representing misuse for pain or recreation/addiction. We trained and evaluated multi-class classifiers, and performed term-level k-means clustering to assess whether there were terms closely associated with the three classes.
resultsOn a held-out test set of 1677 tweets, a transformer-based classifier (XLNet) achieved the best performance with F DISCUSSION: While some past studies have attempted to automatically detect opioid misuse, none have further characterized the motive for misuse. Our multi-class classification approach using XLNet showed promising performance, including in detecting the subtle differences between pain-related and recreation-related misuse. The distinct clustering of class-specific keywords may help conduct targeted data collection, overcoming under-representation of minority classes.
conclusionMachine learning can help identify pain-related and recreational-related OM contents on Twitter to potentially enable the study of the characteristics of individuals exhibiting such behavior.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.