Evidence map›Paper›PMID 35225815›Full record

ArticleJMIR mental health2022

Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review.

Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson

Abstract readScoping Review
In one paragraph

Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Article
  5. Article
  6. Article
  7. 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
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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

5 authors.

Samantha J TeagueCentre for Social and Early Emotional Development, School of Psychology, Deakin University, Geelong, Australia.ORCID https://orcid.org/0000-0002-0487-7307
Adrian B R ShatteSchool of Engineering, Information Technology & Physical Sciences, Federation University, Melbourne, Australia.ORCID https://orcid.org/0000-0002-6225-9697
Emmelyn WellerCentre for Social and Early Emotional Development, School of Psychology, Deakin University, Geelong, Australia.ORCID https://orcid.org/0000-0003-1823-4895
Matthew Fuller-TyszkiewiczCentre for Social and Early Emotional Development, School of Psychology, Deakin University, Geelong, Australia.ORCID https://orcid.org/0000-0003-1145-6057
Delyse M HutchinsonCentre for Social and Early Emotional Development, School of Psychology, Deakin University, Geelong, Australia.ORCID https://orcid.org/0000-0003-3221-7143

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making.

objectiveThis comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications.

methodsA total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data.

resultsApplications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked.

conclusionsThe application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters.

Indexed as

big datadigital psychiatrydisastermental healthSNSsocial media

Identifiers

PMID35225815
PMCPMC8922153

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.