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Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study

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 concernin...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685273/
https://www.ncbi.nlm.nih.gov/pubmed/37962927
http://dx.doi.org/10.2196/45660
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author 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
author_facet 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
author_sort Carabot, Federico
collection PubMed
description 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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spelling pubmed-106852732023-11-30 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 J Med Internet Res Original Paper 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. JMIR Publications 2023-11-14 /pmc/articles/PMC10685273/ /pubmed/37962927 http://dx.doi.org/10.2196/45660 Text en ©Federico Carabot, Carolina Donat-Vargas, Javier Santoma-Vilaclara, Miguel A Ortega, Cielo García-Montero, Oscar Fraile-Martínez, Cristina Zaragoza, Jorge Monserrat, Melchor Alvarez-Mon, Miguel Angel Alvarez-Mon. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.11.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
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
title Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study
title_full Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study
title_fullStr Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study
title_full_unstemmed Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study
title_short Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study
title_sort exploring perceptions about paracetamol, tramadol, and codeine on twitter using machine learning: quantitative and qualitative observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685273/
https://www.ncbi.nlm.nih.gov/pubmed/37962927
http://dx.doi.org/10.2196/45660
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