ArticlePloS one2017
National substance use patterns on Twitter.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 38 citations in OpenAlex.
- Content Analysis of Cannabis Discourses on Twitter/X in the U.S.AJPM focus · 2025Article
- Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study.JMIR infodemiology · 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
- 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
- Diet during the COVID-19 pandemic: An analysis of Twitter data.Patterns (New York, N.Y.) · 2022Article
- Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.Journal of medical Internet research · 2022Article
- Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.International journal of medical informatics · 2021Article
- Emergency Response and Risk Communication Effects of Local Media during COVID-19 Pandemic in China: A Study Based on a Social Media Network.International journal of environmental research and public health · 2021Article
- A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM - population health · 2021Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
- Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study.JMIR public health and surveillance · 2020Article
- A nursing informatics response to COVID-19: Perspectives from five regions of the world.Journal of advanced nursing · 2020Article
- Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study.Journal of medical Internet research · 2020Article
- Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study.PloS one · 2020Article
- Interpol review of controlled substances 2016-2019.Forensic science international. Synergy · 2020Review
- Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter.Journal of biomedical informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
purposeWe examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.
methodsA total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.
resultsQuality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.
conclusionsThe patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.
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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.