Evidence map›Paper›PMID 32429223›Full record

ArticleInternational journal of environmental research and public health2020

Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets.

Viju Raghupathi, Jie Ren, Wullianallur Raghupathi

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Mining Social Media Data to Predict COVID-19 Case Counts.Proceedings. IEEE International Conference on Healthcare Informatics · 2022
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis.International journal of environmental research and public health · 2021
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021
    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

3 authors.

Viju RaghupathiKoppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA.
Jie RenGabelli School of Business, Fordham University, New York, NY 10023, USA.
Wullianallur RaghupathiGabelli School of Business, Fordham University, New York, NY 10023, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Text analysis has been used by scholars to research attitudes toward vaccination and is particularly timely due to the rise of medical misinformation via social media. This study uses a sample of 9581 vaccine-related tweets in the period January 1, 2019 to April 5, 2019. The time period is of the essence because during this time, a measles outbreak was prevalent throughout the United States and a public debate was raging. Sentiment analysis is applied to the sample, clustering the data into topics using the term frequency-inverse document frequency (TF-IDF) technique. The analyses suggest that most (about 77%) of the tweets focused on the search for new/better vaccines for diseases such as the Ebola virus, human papillomavirus (HPV), and the flu. Of the remainder, about half concerned the recent measles outbreak in the United States, and about half were part of ongoing debates between supporters and opponents of vaccination against measles in particular. While these numbers currently suggest a relatively small role for vaccine misinformation, the concept of herd immunity puts that role in context. Nevertheless, going forward, health experts should consider the potential for the increasing spread of falsehoods that may get firmly entrenched in the public mind.

Indexed as

MeaslesSocial MediaVaccinationVaccinesDisease OutbreaksHumansPublic OpinionUnited StatesVaccineshealthcaremeaslessentiment analysissocial mediatext analysisTwittervaccinationvaccine

Identifiers

PMID32429223
PMCPMC7277574

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.