Evidence map›Paper›PMID 32502758›Full record

ArticleComputers in biology and medicine2020

A scoping review of the use of Twitter for public health research.

Oduwa Edo-Osagie, Beatriz De La Iglesia, Iain Lake, Obaghe Edeghere

Abstract readScoping Review
In one paragraph

Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 2 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

56 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
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  8. Identifying Dietary Supplements Related Effects from Social Media by ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
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  11. Observational
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  14. A Taxonomy for Health Information Systems.Journal of medical Internet research · 2024
    Review
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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

4 authors.

Oduwa Edo-OsagieSchool of Computing Science, University of East Anglia, Norwich, NR4 7TJ, UK. Electronic address: o.edo-osagie@uea.ac.uk.
Beatriz De La IglesiaSchool of Computing Science, University of East Anglia, Norwich, NR4 7TJ, UK.
Iain LakeSchool of Environmental Science, University of East Anglia, Norwich, NR4 7TJ, UK.
Obaghe EdeghereNational Infection Service, Public Health England, Birmingham, B3 2PW, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.

Indexed as

Social MediaCommunicationHumansPublic HealthDisease trackingEvent forecastingPharmacovigilancePublic healthSyndromic surveillance

Identifiers

PMID32502758
PMCPMC7229729

What OpenQuestion holds

Textmetadata
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.