Evidence map›Paper›PMID 40906819›Full record

ArticlePloS one2025

Public perception and changing attitudes toward antidepressants over a decade in social media: Lessons learned from online discussion using artificial intelligence.

Min Ho An, Min-Gyu Kim, Jueon Kim, Seheon Chang, Dong Yun Lee, Rae Woong Park

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Min Ho AnDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0003-2773-9756
Min-Gyu KimDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0009-0001-8243-0287
Jueon KimDepartment of Dentistry, Kyung Hee University School of Dentistry, Seoul, Republic of Korea.
Seheon ChangCNS Department of Computer Science, University of Texas at Austin, Austin, Texas, United States of America.
Dong Yun LeeDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0002-3678-9862
Rae Woong ParkDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0003-4989-3287

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients' perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public's perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline.

methodsThis study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann-Kendall test to evaluate shifts in sentiments over time.

resultsWe analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster.

conclusionsNegative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.

Indexed as

Antidepressive AgentsArtificial IntelligencePublic OpinionSocial MediaDepressionHumansAntidepressive Agents

Identifiers

PMID40906819
PMCPMC12410866

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.