ArticleAmerican journal of public health2017
Twitter-Based Detection of Illegal Online Sale of Prescription Opioid.
Article in American journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions.Science and engineering ethics · 2020Pooled it
- Quantitative assessment of china's policies on the online sale of prescription drugs.PloS one · 2026Article
- Platforms, risk perceptions, and reporting: the impact of illicit drug advertisements on social media among UK secondary students.Harm reduction journal · 2025Article
- Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis.Journal of medical Internet research · 2025Article
- The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.JMIR infodemiology · 2024Article
- Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers.Cannabis and cannabinoid research · 2024Article
- Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.JMIR public health and surveillance · 2024Article
- Forecasting drug-overdose mortality by age in the United States at the national and county levels.PNAS nexus · 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
- Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis.Substance use & misuse · 2024Article
- Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.Journal of medical Internet research · 2023Observational
- Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study.Journal of medical Internet research · 2023Article
- Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework.Journal of medical Internet research · 2023Article
- Illicit Online Pharmacies: A Scoping Review.International journal of environmental research and public health · 2023Article
- Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- Article
- Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study.JMIR public health and surveillance · 2022Article
- Human Decision-making in an Artificial Intelligence-Driven Future in Health: Protocol for Comparative Analysis and Simulation.JMIR research protocols · 2022Article
- An unsupervised machine learning approach for the detection and characterization of illicit drug-dealing comments and interactions on Instagram.Substance abuse · 2022Article
- Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US.Annals of GIS · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances.
methodsWe first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015.
resultsA total of 1778 tweets (< 1%) were identified as marketing the sale of controlled substances online; 90% had imbedded hyperlinks, but only 46 were "live" at the time of the evaluation. Seven distinct URLs linked to Web sites marketing or illegally selling controlled substances online.
conclusionsOur methodology can identify illegal online sale of prescription opioids from large volumes of tweets. Our results indicate that controlled substances are trafficked online via different strategies and vendors. Public Health Implications. Our methodology can be used to identify illegal online sellers in criminal violation of the Ryan Haight Online Pharmacy Consumer Protection Act.
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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.