ArticleJournal of medical toxicology : official journal of the American College of Medical Toxicology2017
Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media.
Article in Journal of medical toxicology : official journal of the American College of Medical Toxicology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Prevalence of Health Misinformation on Social Media: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Curation and Extraction of Drug-Related Entities from Reddit Platform.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Monitoring the opioid epidemic via social media discussions.NPJ digital medicine · 2025Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- Artificial intelligence in epidemic watch: revolutionizing infectious diseases surveillance.Frontiers in digital health · 2025Review
- 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
- Discovering opioid slang on social media: A Word2Vec approach with reddit data.Drug and alcohol dependence reports · 2024Article
- Content analysis of Reddit posts about coadministration of selective serotonin reuptake inhibitors and psilocybin mushrooms.Psychopharmacology · 2024Article
- Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study.Journal of medical Internet research · 2023Article
- 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
- Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.Journal of medical Internet research · 2023Article
- Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data.Scientific reports · 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
- Exploring content of misinformation about HPV vaccine on twitter.Journal of behavioral medicine · 2023Article
- Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- #ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.Health data science · 2023Article
- Biases in using social media data for public health surveillance: A scoping review.International journal of medical informatics · 2022Article
- Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources.JAMA network open · 2022Article
- Machine Learning in Health Promotion and Behavioral Change: Scoping Review.Journal of medical Internet research · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month.
methodsWe wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH).
resultsTweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage.
conclusionsMentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.
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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.