Evidence map›Paper›PMID 35207644›Full record

ArticleJournal of personalized medicine2022

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

Laura de Anta, Miguel Angel Alvarez-Mon, Miguel A Ortega, Cristina Salazar, Carolina Donat-Vargas, Javier Santoma-Vilaclara, Maria Martin-Martinez, Guillermo Lahera, Luis Gutierrez-Rojas, Roberto Rodriguez-Jimenez and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Article
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  4. Observational
  5. Understanding social media discourse on antidepressants: unsupervised and sentiment analysis using X.European psychiatry : the journal of the Association of European Psychiatrists · 2025
    Article
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  7. Article
  8. Observational
  9. Article
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  12. Antidepressants: A content analysis of healthcare providers' tweets.Exploratory research in clinical and social pharmacy · 2023
    Article
  13. Article
  14. Assessment of beliefs and attitudes about electroconvulsive therapy posted on Twitter: An observational study.European psychiatry : the journal of the Association of European Psychiatrists · 2023
    Observational
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 2 countries.

Laura de AntaDepartment of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain.
Miguel Angel Alvarez-MonDepartment of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain.
Miguel A OrtegaDepartment of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain.ORCID 0000-0003-2588-1708
Cristina SalazarDepartamento Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Escuela Técnica Superior de Ingeniería de Telecomunicación, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain.ORCID 0000-0002-2373-0909
Carolina Donat-VargasUnit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (IMM), Karolinska Institute, 171 77 Stockholm, Sweden.ORCID 0000-0002-4523-4148
Javier Santoma-VilaclaraIBM Data and AI Expert Labs and Learning, London WC2N 5DU, UK.
Maria Martin-MartinezDepartment of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain.
Guillermo LaheraDepartment of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain.ORCID 0000-0002-6152-3564
Luis Gutierrez-RojasPsychiatry Service, Hospital Clínico San Cecilio, 18016 Granada, Spain.ORCID 0000-0003-0082-2189
Roberto Rodriguez-JimenezCIBERSAM (Biomedical Research Networking Centre in Mental Health), 22807 Madrid, Spain.ORCID 0000-0003-2251-7249
Javier QuinteroDepartment of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain.ORCID 0000-0002-2491-8647
Melchor Alvarez-MonDepartment of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain.ORCID 0000-0003-1309-7510
Universidad de Alcalá · ESHospital Universitario Infanta Leonor · ESUniversidad Complutense de Madrid · ESUniversidad Rey Juan Carlos · ESKarolinska Institutet · SE

Funding

Comunidad de Madrid B2017/BMD-3804Instituto de Salud Carlos III PI18/01726
6 · The paper itself

Abstract

backgroundAntidepressants 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.

methodsWe 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.

resultsThe 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%).

conclusionsThis 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.

Indexed as

antidepressantsartificial intelligencedepressionmachine learningpharmacoepidemiologypsychopharmacology

Identifiers

PMID35207644
PMCPMC8879287
OpenAlexW4214657978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.