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Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data.
Saha, Koustuv; Torous, John; Kiciman, Emre; De Choudhury, Munmun.
  • Saha K; Georgia Institute of Technology, Atlanta, GA, United States.
  • Torous J; Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
  • Kiciman E; Microsoft Research, Redmond, WA, United States.
  • De Choudhury M; Georgia Institute of Technology, Atlanta, GA, United States.
JMIR Ment Health ; 8(3): e26589, 2021 Mar 19.
Article in English | MEDLINE | ID: covidwho-1160624
Semantic information from SemMedBD (by NLM)
1. Challenge COEXISTS_WITH Mental health treatment
Subject
Challenge
Predicate
COEXISTS_WITH
Object
Mental health treatment
2. Antidepressive Agents ADMINISTERED_TO Persons
Subject
Antidepressive Agents
Predicate
ADMINISTERED_TO
Object
Persons
3. Challenge COEXISTS_WITH Mental health treatment
Subject
Challenge
Predicate
COEXISTS_WITH
Object
Mental health treatment
4. Antidepressive Agents ADMINISTERED_TO Persons
Subject
Antidepressive Agents
Predicate
ADMINISTERED_TO
Object
Persons
ABSTRACT

BACKGROUND:

Antidepressants 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.

OBJECTIVE:

We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.

METHODS:

On 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.

RESULTS:

Five 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.

CONCLUSIONS:

This 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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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Language: English Journal: JMIR Ment Health Year: 2021 Document Type: Article Affiliation country: 26589

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Language: English Journal: JMIR Ment Health Year: 2021 Document Type: Article Affiliation country: 26589