SynthesisJournal of public health (Oxford, England)2017
Systematic review of surveillance by social media platforms for illicit drug use.
Synthesis in Journal of public health (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 55 citations in OpenAlex.
- Methods for Detecting Suspicious Information From Individual Transactions of Pharmaceutical Products via Twitter (now X): Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Multilingual analysis of public discourse on opioid and non-opioid analgesics through social media: a cross-sectional infodemiological study.BMC medical research methodology · 2026Article
- An age-period-cohort-interaction analysis of meth/amphetamine-related deaths in Australia, 2001-2020.Addiction (Abingdon, England) · 2025Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- Substance Use Monitoring Among People Who Use Drugs Using a Novel Assay to Test Dried Blood Spot Specimens.Substance use & misuse · 2025Article
- Structure and dynamics mapping of illicit firearms trafficking using artificial intelligence models.Frontiers in big data · 2025Article
- Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework.Journal of medical Internet research · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Social media and internet search data to inform drug utilization: A systematic scoping review.Frontiers in digital health · 2023Article
- Social media discourse and internet search queries on cannabis as a medicine: A systematic scoping review.PloS one · 2023Article
- Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study.Journal of medical Internet research · 2022Article
- Social Media and Health Care (Part II): Narrative Review of Social Media Use by Patients.Journal of medical Internet research · 2022Review
- Personal and Social Consequences of Psychotropic Substance Use: A Population-Based Internet Survey.Medicina (Kaunas, Lithuania) · 2022Article
- Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification.Frontiers in big data · 2022Article
- Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review.Yearbook of medical informatics · 2021Review
- Representations of Psychoactive Drugs' Use in Mass Culture and Their Impact on Audiences.International journal of environmental research and public health · 2021Review
- Socially-supportive norms and mutual aid of people who use opioids: An analysis of Reddit during the initial COVID-19 pandemic.Drug and alcohol dependence · 2021Article
- Associations Between Substance Use and Instagram Participation to Inform Social Network-Based Screening Models: Multimodal Cross-Sectional Study.Journal of medical Internet research · 2020Article
- Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis.Journal of medical Internet research · 2020Article
- Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study.JMIR public health and surveillance · 2020Article
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
Background: The use of social media (SM) as a surveillance tool of global illicit drug use is limited. To address this limitation, a systematic review of literature focused on the ability of SM to better recognize illicit drug use trends was addressed. Methods: A search was conducted in databases: PubMed, CINAHL via Ebsco, PsychINFO via Ebsco, Medline via Ebsco, ERIC, Cochrane Library, Science Direct, ABI/INFORM Complete and Communication and Mass Media Complete. Included studies were original research published in peer-reviewed journals between January 2005 and June 2015 that primarily focused on collecting data from SM platforms to track trends in illicit drug use. Excluded were studies focused on purchasing prescription drugs from illicit online pharmacies. Results: Selected studies used a range of SM tools/applications, including message boards, Twitter and blog/forums/platform discussions. Limitations included relevance, a lack of standardized surveillance systems and a lack of efficient algorithms to isolate relevant items. Conclusion: Illicit drug use is a worldwide problem, and the rise of global social networking sites has led to the evolution of a readily accessible surveillance tool. Systematic approaches need to be developed to efficiently extract and analyze illicit drug content from social networks to supplement effective prevention programs.
Indexed as
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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.