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Areas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study

dc.contributor.authorAnta, Laura de
dc.contributor.authorÁlvarez Mon, Miguel Ángel
dc.contributor.authorOrtega Campos, Miguel Ángel
dc.contributor.authorSalazar, Cristina
dc.contributor.authorDonat Vargas, Carolina
dc.contributor.authorSantoma Vilaclara, Javier
dc.contributor.authorMartín Martínez, María
dc.contributor.authorLahera, Guillermo
dc.contributor.authorGutierrez Rojas, Luis
dc.contributor.authorRodríguez Jiménez, Roberto
dc.contributor.authorQuintero, Javier
dc.contributor.authorÁlvarez Mon, Melchor
dc.date.accessioned2023-06-22T10:49:20Z
dc.date.available2023-06-22T10:49:20Z
dc.date.issued2022-03-28
dc.description.abstractBackground: Antidepressants are the foundation of the treatment of major depressive disorders. Despite the scientific evidence, there is still a sustained debate and concern about the efficacy of antidepressants, with widely differing opinions among the population about their positive and negative effects, which may condition people’s attitudes towards such treatments. Our aim is to investigate Twitter posts about antidepressants in order to have a better understanding of the social consideration of antidepressants. Methods: We gathered public tweets mentioning antidepressants written in English, published throughout a 22-month period, between 1 January 2019 and 31 October 2020. We analysed the content of each tweet, determining in the first place whether they included medical aspects or not. Those with medical content were classified into four categories: general aspects, such as quality of life or mood, sleep-related conditions, appetite/weight issues and aspects around somatic alterations. In non-medical tweets, we distinguished three categories: commercial nature (including all economic activity, drug promotion, education or outreach), help request/offer, and drug trivialization. In addition, users were arranged into three categories according to their nature: patients and relatives, caregivers, and interactions between Twitter users. Finally, we identified the most mentioned antidepressants, including the number of retweets and likes, which allowed us to measure the impact among Twitter users. Results: The activity in Twitter concerning antidepressants is mainly focused on the effects these drugs may have on certain health-related areas, specifically sleep (20.87%) and appetite/weight (8.95%). Patients and relatives are the type of user that most frequently posts tweets with medical content (65.2%, specifically 80% when referencing sleep and 78.6% in the case of appetite/weight), whereas they are responsible for only 2.9% of tweets with non-medical content. Among tweets classified as non-medical in this study, the most common subject was drug trivialization (66.86%). Caregivers barely have any presence in conversations in Twitter about antidepressants (3.5%). However, their tweets rose more interest among other users, with a ratio 11.93 times higher than those posted by patients and their friends and family. Mirtazapine is the most mentioned antidepressant in Twitter (45.43%), with a significant difference with the rest, agomelatine (11.11%). Conclusions: This study shows that Twitter users that take antidepressants, or their friends and family, use social media to share medical information about antidepressants. However, other users that do not talk about antidepressants from a personal or close experience, frequently do so in a stigmatizing manner, by trivializing them. Our study also brings to light the scarce presence of caregivers in Twitter.
dc.description.departmentDepto. de Medicina
dc.description.facultyFac. de Medicina
dc.description.refereedTRUE
dc.description.sponsorshipInstituto de Salud Carlos III (ISCIII)
dc.description.sponsorshipComunidad de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/73357
dc.identifier.doi10.3390/jpm12020155
dc.identifier.issn2075-4426
dc.identifier.officialurlhttps://doi.org/10.3390/jpm12020155
dc.identifier.relatedurlhttps://www.mdpi.com/2075-4426/12/2/155/htm
dc.identifier.urihttps://hdl.handle.net/20.500.14352/71729
dc.issue.number2
dc.journal.titleJournal of Personalized Medicine
dc.language.isoeng
dc.page.initial155
dc.publisherMPDI
dc.relation.projectID(PI18/01726; PI19/00766)
dc.relation.projectID(B2017/BMD-3804)
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.keyworddepression
dc.subject.keywordpsychopharmacology
dc.subject.keywordantidepressants
dc.subject.keywordmachine learning
dc.subject.keywordartificial intelligence
dc.subject.keywordpharmacoepidemiology
dc.subject.ucmFarmacología (Medicina)
dc.subject.ucmPsicología (Psicología)
dc.subject.ucmNeuropsicología
dc.subject.unesco61 Psicología
dc.titleAreas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study
dc.typejournal article
dc.volume.number12
dspace.entity.typePublication
relation.isAuthorOfPublication4843ea93-2d0c-4160-9d4a-6df323db4323
relation.isAuthorOfPublication773f92bc-2db7-4bf8-ab49-1ac5c630b487
relation.isAuthorOfPublication.latestForDiscovery4843ea93-2d0c-4160-9d4a-6df323db4323

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