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Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.
Carabot, Federico; Donat-Vargas, Carolina; Santoma-Vilaclara, Javier; Ortega, Miguel A; García-Montero, Cielo; Fraile-Martínez, Oscar; Zaragoza, Cristina; Monserrat, Jorge; Alvarez-Mon, Melchor; Alvarez-Mon, Miguel Angel.
Afiliação
  • Carabot F; Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain.
  • Donat-Vargas C; Ramon y Cajal Institute of Sanitary Research, Madrid, Spain.
  • Santoma-Vilaclara J; Institute of Environmental Medicine, Karolinska Institutet, Unit of Cardiovascular and Nutritional Epidemiology, Stockholm, Sweden.
  • Ortega MA; ISGlobal, Institut de Salut Global de Barcelona, Campus MAR, Barcelona, Spain.
  • García-Montero C; Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Madrid, Spain.
  • Fraile-Martínez O; Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain.
  • Zaragoza C; Data & AI, Filament Consultancy Group., London, United Kingdom.
  • Monserrat J; Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain.
  • Alvarez-Mon M; Ramon y Cajal Institute of Sanitary Research, Madrid, Spain.
  • Alvarez-Mon MA; Cancer Registry and Pathology Department, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain.
J Med Internet Res ; 25: e45660, 2023 11 14.
Article em En | MEDLINE | ID: mdl-37962927
ABSTRACT

BACKGROUND:

Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction.

OBJECTIVE:

This study aims to explore the perceptions and experiences of Twitter users concerning these drugs.

METHODS:

We 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.

RESULTS:

Of 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).

CONCLUSIONS:

Patients 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tramadol / Mídias Sociais Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tramadol / Mídias Sociais Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha