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DOI 10.2196/45660

Медицинская библиотека · Journal of medical Internet research · 2023

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

Наблюдательное исследованиеСтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: 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. Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.. Journal of medical Internet research. 2023. DOI: 10.2196/45660.

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