ArticleJMIR mental health2021
Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the evidence: a systematic review of reviews of the effectiveness and safety of digital interventions for ADHD.BMC psychiatry · 2025Pooled it
- Dashboard Intervention for Tracking Digital Social Media Activity in the Clinical Care of Individuals With Mood and Anxiety Disorders: Randomized Trial.JMIR mental health · 2025Trial
- Mapping Peer Support in Antidepressant Discussions on Reddit: Pilot Network Analysis.Journal of participatory medicine · 2026Article
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- Societal perspectives on psychedelics use in clinical context: Development of Concerns and Openness towards Psychedelic Scale (COPS).Dialogues in clinical neuroscience · 2025Article
- Delving into the Perception, Use, and Context of Duloxetine in Clinical Practice: An Analysis Based on the Experience of Healthcare Professionals.Brain sciences · 2025Article
- Testing a Dashboard Intervention for Tracking Digital Social Media Activity in Clinical Care of Individuals With Mood and Anxiety Disorders: Protocol and Design Considerations for a Pragmatic Randomized Trial.JMIR research protocols · 2025Article
- Probing Public Perceptions of Antidepressants on Social Media: Mixed Methods Study.JMIR formative research · 2025Article
- Antidepressant Potential of Hispidulin Present inCentral nervous system agents in medicinal chemistry · 2025Article
- A large-scale observational comparison of antidepressants and their effects.Journal of psychiatric research · 2024Observational
- Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.BMC medical informatics and decision making · 2024Article
- Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach.JMIR formative research · 2024Article
- Robust language-based mental health assessments in time and space through social media.NPJ digital medicine · 2024Article
- Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.Journal of medical Internet research · 2023Observational
- Text as signal. A tutorial with case studies focusing on social media (Twitter).Behavior research methods · 2023Article
- Attitudes and (Mis)information About Cognitive Behavioral Therapy on TikTok: An Analysis of Video Content.Journal of medical Internet research · 2023Article
- Overcoming treatment gaps in the management of depression with non-pharmacological adjunctive strategies.Frontiers in psychiatry · 2023Review
- Editorial: Plant secondary metabolites: Potential therapeutic implications in neuropsychiatric disorders.Frontiers in behavioral neuroscience · 2023Article
- Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.Proceedings of the ACM on human-computer interaction · 2022Article
- A call for open data to develop mental health digital biomarkers.BJPsych open · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundAntidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants.
objectiveWe aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.
methodsOn a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018.
resultsFive major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects.
conclusionsThis work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants.
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Registered trials
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.