Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study

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• dc.contributor.author Anta, Laura de
• dc.contributor.author Álvarez-Mon, Miguel Ángel
• dc.contributor.author Pereira-Sanchez, Victor
• dc.contributor.author Donat-Vargas, Carolina
• dc.contributor.author Lara-Abelenda, Francisco J.
• dc.contributor.author Arrieta, María
• dc.contributor.author Montero Torres, María
• dc.contributor.author García Montero, Cielo
• dc.contributor.author Fraile Martínez, Óscar
• dc.contributor.author Mora, Fernando
• dc.contributor.author Ortega, Miguel Ángel
• dc.contributor.author Alvarez-Mon, Melchor
• dc.contributor.author Quintero, Javier
• dc.date.accessioned 2024-11-28T08:27:09Z
• dc.date.available 2024-11-28T08:27:09Z
• dc.date.issued 2024
• dc.description.abstract Background: Benzodiazepines are frequently prescribed drugs; however, their prolonged use can lead to tolerance, dependence, and other adverse effects. Despite these risks, long-term use remains common, presenting a public health concern. This study aims to explore the beliefs and opinions held by the public regarding benzodiazepines, as understanding these perspectives may provide insights into their usage patterns. Methods: We collected public tweets published in English between January 1, 2019, and October 31, 2020, that mentioned benzodiazepines. The content of each tweet and the characteristics of the users were analyzed using a mixed-method approach, including manual analysis and semi-supervised machine learning. Results: Over half of the Twitter users highlighted the efficacy of benzodiazepines, with minimal discussion of their side effects. The most active participants in these conversations were patients and their families, with health professionals and institutions being notably absent. Additionally, the drugs most frequently mentioned corresponded with those most commonly prescribed by healthcare professionals. Conclusions: Social media platforms offer valuable insights into users' experiences and opinions regarding medications. Notably, the sentiment towards benzodiazepines is predominantly positive, with users viewing them as effective while rarely mentioning side effects. This analysis underscores the need to educate physicians, patients, and their families about the potential risks associated with benzodiazepine use and to promote clinical guidelines that support the proper management of these medications. Clinical trial number: Not applicable.
• dc.format.mimetype application/pdf
• dc.identifier.citation Anta L, Alvarez-Mon MÁ, Pereira-Sanchez V, Donat-Vargas CC, Lara-Abelenda FJ, Arrieta M, et al. Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study. BMC Psychiatry. 2024 Oct 8;24(1):659. DOI: 10.1186/s12888-024-06111-5
• dc.identifier.doi http://dx.doi.org/10.1186/s12888-024-06111-5
• dc.identifier.issn 1471-244X
• dc.identifier.uri http://hdl.handle.net/10230/68847
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof BMC Psychiatry. 2024 Oct 8;24(1):659
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Benzodiazepines
• dc.subject.keyword Psychopharmacology
• dc.subject.keyword Twitter
• dc.subject.keyword Social media
• dc.subject.keyword Public opinion
• dc.title Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion