Evidence map›Paper›PMID 36146564›Full record

ArticleVaccines2022

Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

Zifu Wang, Yudi Chen, Yun Li, Devika Kakkar, Wendy Guan, Wenying Ji, Jacob Cain, Hai Lan, Dexuan Sha, Qian Liu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. 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
1.1field-weighted citation impact, top 22% 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, 8 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

11 authors at 3 institutions in 1 country.

Zifu WangDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0002-7183-5166
Yudi ChenDepartment of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA.
Yun LiDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0002-3205-8464
Devika KakkarCenter for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA.
Wendy GuanCenter for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA.ORCID 0000-0002-4813-7546
Wenying JiDepartment of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0002-1222-2191
Jacob CainDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.
Hai LanDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0002-4119-4388
Dexuan ShaDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.
Qian LiuDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0003-3876-4877
Chaowei YangDepartment of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0001-7768-4066
George Mason University · USHarvard University · USFrostburg State University · US

Funding

National Science Foundation 1841520, 1835507, 1832465, 2028791 and 2025783
6 · The paper itself

Abstract

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans' sentiment toward vaccines was relatively lower than other races.

Indexed as

bayesian inferenceCOVID-19 vaccinespublic opinionsrace inequalitysocial mediaspatiotemporal analysis

Identifiers

PMID36146564
PMCPMC9504395
OpenAlexW4295094747

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.