Evidence map›Paper›PMID 36287997›Full record

ArticleTropical medicine and infectious disease2022

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation.

Victor Diogho Heuer de Carvalho, Thyago Celso Cavalcante Nepomuceno, Thiago Poleto, Jean Gomes Turet, Ana Paula Cabral Seixas Costa

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Victor Diogho Heuer de CarvalhoEixo das Tecnologias, Campus do Sertão, Federal University of Alagoas, Delmiro Gouveia 57480-000, Brazil.ORCID 0000-0003-2369-7317
Thyago Celso Cavalcante NepomucenoNúcleo de Tecnologia, Centro Acadêmico do Agreste, Federal University of Pernambuco, Caruaru 55014-900, Brazil.ORCID 0000-0001-8327-6472
Thiago PoletoDepartamento de Administração, Federal University of Pará, Belém 66075-110, Brazil.ORCID 0000-0001-7100-2319
Jean Gomes TuretDepartamento de Engenharia de Produção, Federal University of Pernambuco, Recife 50740-550, Brazil.
Ana Paula Cabral Seixas CostaDepartamento de Engenharia de Produção, Federal University of Pernambuco, Recife 50740-550, Brazil.ORCID 0000-0002-2932-4784

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

Indexed as

BrazilCOVID-19misinformationopinion miningpandemicstemporal analysistwitter datavaccination

Identifiers

PMID36287997
PMCPMC9607799

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.