Evidence map›Paper›PMID 33156805›Full record

ArticleJournal of medical Internet research2020

Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media.

Koustuv Saha, John Torous, Eric D Caine, Munmun De Choudhury

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 3 pooled it
–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

61 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Twitter reveals spatio-temporal variation in vaccine concerns in Sub-Saharan Africa.medRxiv : the preprint server for health sciences · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

1 more citing papers are in PubMed but not listed here.

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

4 authors.

Koustuv SahaSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0000-0002-8872-2934
John TorousDivision of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-5362-7937
Eric D CaineDepartment of Psychiatry, University of Rochester, Rochester, NY, United States.ORCID 0000-0002-4859-9679
Munmun De ChoudhurySchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.ORCID 0000-0002-8939-264X

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
Social Media Signals for Post-traumatic Stress and Anxiety in Crisis-Inflicted CommunitiesR01GM112697 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2014 to 2018
$1.4M
NIGMS NIH HHS R01 GM112697NIMH NIH HHS R01 MH117172
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.

objectiveOur study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.

methodsWe obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets.

resultsWe found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis.

conclusionsWe studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.

Indexed as

Social MediaCOVID-19HumansNon-Randomized Controlled Trials as TopicPsychologySARS-CoV-2anxietycoronavirusCOVID-19crisisdepressionemotionslanguagemental healthpsychosocial effectssocial mediasocial supportstresstransfer learningTwitter

Identifiers

PMID33156805
PMCPMC7690250

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.