Evidence map›Paper›PMID 36416280›Full record

ArticleHealth informatics journal

A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset.

Tareq Nasralah, Ahmed Elnoshokaty, Omar El-Gayar, Mohammad Al-Ramahi, Abdullah Wahbeh

Open access · goldAbstract read
In one paragraph

Article in Health informatics journal. 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
1.5field-weighted citation impact, top 18% of its field
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, 9 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 5 institutions in 1 country.

Tareq NasralahSupply Chain and Information Management Group, D'Amore-McKim School of Business, 1848Northeastern University, Boston, MA, USA.ORCID 0000-0002-2336-7601
Ahmed Elnoshokaty5336Northern Michigan University, Marquette, MI, United States.ORCID 0000-0003-3844-3985
Omar El-Gayar40975Dakota State University, Madison, SD, United States.
Mohammad Al-Ramahi215387Texas A&M University-San Antonio, San Antonio, TX, United States.ORCID 0000-0002-8170-8312
Abdullah WahbehSlippery Rock University of Pennsylvania, Slippery Rock, PA, United States.ORCID 0000-0002-6894-0192
Dakota State University · USNortheastern University · USNorthern Michigan University · USSlippery Rock University · USTexas A&M University – San Antonio · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, "government and politics" and "conspiracy theories," and decreased for "developmental disabilities." Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.

Indexed as

COVID-19MeaslesSocial MediaCOVID-19 VaccinesHumansPublic HealthCOVID-19 Vaccinesanalyticsanti-vaxxersCOVID-19social mediavaccines

Identifiers

PMID36416280
PMCPMC9692178
OpenAlexW4309768764

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.