ArticleJournal of medical Internet research2015
Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 101 citations in OpenAlex.
- Prevalence of Health Misinformation on Social Media: Systematic Review.Journal of medical Internet research · 2021Pooled it
- What About Drug Checking? Systematic Review and Netnographic Analysis of Social Media.Current neuropharmacology · 2020Pooled it
- The Grey Market for Alcohol on Social Media in Sweden.Nordisk alkohol- & narkotikatidskrift : NAT · 2026Article
- Monitoring the opioid epidemic via social media discussions.NPJ digital medicine · 2025Article
- Large-Scale Deep Learning-Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic.JMIR infodemiology · 2025Article
- Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit.Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media · 2025Article
- Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.Journal of medical Internet research · 2024Article
- Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study.JMIR formative research · 2024Article
- Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.Journal of medical Internet research · 2023Observational
- Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.Journal of medical Internet research · 2023Article
- Illicit Online Pharmacies: A Scoping Review.International journal of environmental research and public health · 2023Article
- Article
- Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature.Frontiers in public health · 2023Article
- Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources.JAMA network open · 2022Article
- The Impact of Government Social Media Information Quality on Public Panic During the Infodemic.Frontiers in psychology · 2022Article
- Re-evaluating standards of human subjects protection for sensitive health data in social media networks.Social networks · 2021Article
- Detection of emerging drugs involved in overdose via diachronic word embeddings of substances discussed on social media.Journal of biomedical informatics · 2021Article
- Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study.JMIR public health and surveillance · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Strengths and weaknesses of existing data sources to support research to address the opioids crisis.Preventive medicine reports · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundYouth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens.
objectiveIn order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies.
methodsTweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning.
resultsA total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription.
conclusionsThis study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online.
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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.