Observational studyJournal of medical Internet research2023
Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.
Observational study in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- The effect of source expertise on the persuasiveness and sharing of health information on social media: A systematic review.Psychonomic bulletin & review · 2026Pooled it
- Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026Review
- Multilingual analysis of public discourse on opioid and non-opioid analgesics through social media: a cross-sectional infodemiological study.BMC medical research methodology · 2026Article
- Exploring Pain on Social Media: Observational Study on Perceptions and Discussions of Chronic Pain Conditions.JMIR infodemiology · 2025Observational
- Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis.JMIR infodemiology · 2025Article
- Which social media platforms facilitate monitoring the opioid crisis?PLOS digital health · 2025Article
- Understanding social media discourse on antidepressants: unsupervised and sentiment analysis using X.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Analyzing public discourse of dementia from Spanish and English tweets: a comparative analysis with other neurological disorders.Frontiers in neurology · 2024Article
- Regional insights on tobacco-related tweets: unveiling user opinions and usage patterns.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParacetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction.
objectiveThis study aims to explore the perceptions and experiences of Twitter users concerning these drugs.
methodsWe analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers.
resultsOf classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8%). Among medical content tweets, 80.8% (41,080/50,822) mentioned drug efficacy, with only 6.9% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001).
conclusionsPatients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions.
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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.