ArticleDrug and alcohol review2020
Detecting illicit opioid content on Twitter.
Article in Drug and alcohol review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study.JMIR infodemiology · 2025Article
- #Opioids: A Mixed-Methods Examination of Top Opioid-Related Content on TikTok.Journal of studies on alcohol and drugs · 2025Article
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.Journal of medical Internet research · 2024Article
- Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine.Frontiers in psychiatry · 2024Article
- Types and Sources of Stigma on Opioid Use Treatment and Recovery Communities on Reddit.Substance use & misuse · 2022Article
- Harm reduction via online platforms for people who use drugs in Russia: a qualitative analysis of web outreach work.Harm reduction journal · 2020Article
- Harm reduction via online platforms for people who use drugs in Russia: A qualitative analysis of web outreach work.Research square · 2020Article
- Assessing perceptions about medications for opioid use disorder and Naloxone on Twitter.Journal of addictive diseasesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
INTRODUCTION AND
aimsThis article examines the feasibility of leveraging Twitter to detect posts authored by people who use opioids (PWUO) or content related to opioid use disorder (OUD), and manually develop a multidimensional taxonomy of relevant tweets. DESIGN AND
methodsTwitter messages were collected between June and October 2017 (n = 23 827) and evaluated using an inductive coding approach. Content was then manually classified into two axes (n = 17 420): (i) user experience regarding accessing, using, or recovery from illicit opioids; and (ii) content categories (e.g. policies, medical information, jokes/sarcasm).
resultsThe most prevalent categories consisted of jokes or sarcastic comments pertaining to OUD, PWUOs or hypothetically using illicit opioids (63%), informational content about treatments for OUD, overdose prevention or accessing self-help groups (20%), and commentary about government opioid policy or news related to opioids (17%). Posts by PWUOs centered on identifying illicit sources for procuring opioids (i.e. online, drug dealers; 49%), symptoms and/or strategies to quell opioid withdrawal symptoms (21%), and combining illicit opioid use with other substances, such as cocaine or benzodiazepines (17%). State and public health experts infrequently posted content pertaining to OUD (1%). DISCUSSION AND
conclusionsTwitter offers a feasible approach to identify PWUO. Further research is needed to evaluate the efficacy of Twitter to disseminate evidence-based content and facilitate linkage to treatment and harm reduction services.
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What OpenQuestion holds
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.