Evidence map›Paper›PMID 35789666›Full record

ArticleDiscover mental health2022

Opportunities and challenges of using social media big data to assess mental health consequences of the COVID-19 crisis and future major events.

Martin Tušl, Anja Thelen, Kailing Marcus, Alexandra Peters, Evgeniya Shalaeva, Benjamin Scheckel, Martin Sykora, Suzanne Elayan, John A Naslund, Ketan Shankardass and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover mental health, 2022. 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. The Role of Social Media in Building Pandemic Resilience in an Urban Community: A Qualitative Case Study.International journal of environmental research and public health · 2023
    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

13 authors.

Martin TušlPublic and Organizational Health, Epidemiology, Biostatistics, and Prevention Institute, Center of Salutogenesis, University of Zurich, Zurich, Switzerland.
Anja ThelenInstitute for Health Economics and Clinical Epidemiology, The University Hospital of Cologne, University of Cologne, Cologne, Germany.
Kailing MarcusFaculty of Medicine, University of Geneva, Geneva, Switzerland.
Alexandra PetersInfection Control Programme and WHO Collaborating Center on Patient Safety, University of Geneva, Geneva, Switzerland.
Evgeniya ShalaevaDivision of Public Health Science, Westminster International University in Tashkent, Tashkent, Uzbekistan.
Benjamin ScheckelInstitute for Health Economics and Clinical Epidemiology, The University Hospital of Cologne, University of Cologne, Cologne, Germany.
Martin SykoraCentre for Information Management, Loughborough University, Loughborough, Leicestershire UK.
Suzanne ElayanCentre for Information Management, Loughborough University, Loughborough, Leicestershire UK.
John A NaslundDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA USA.
Ketan ShankardassDepartment of Health Sciences, Wilfrid Laurier University, Waterloo, ON Canada.
Stephen J MooneyDepartment of Epidemiology, University of Washington, Seattle, WA USA.
Marta FaddaFaculty of Biomedical Sciences, University of Lugano, Lugano, Switzerland.
Oliver GruebnerDepartment of Geography, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present commentary discusses how social media big data could be used in mental health research to assess the impact of major global crises such as the COVID-19 pandemic. We first provide a brief overview of the COVID-19 situation and the challenges associated with the assessment of its global impact on mental health using conventional methods. We then propose social media big data as a possible unconventional data source, provide illustrative examples of previous studies, and discuss the advantages and challenges associated with their use for mental health research. We conclude that social media big data represent a valuable resource for mental health research, however, several methodological limitations and ethical concerns need to be addressed to ensure safe use.

Indexed as

Big dataCOVID-19Mental healthSocial media

Identifiers

PMID35789666
PMCPMC9243703

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.