Evidence map›Paper›PMID 35331966›Full record

ArticleJournal of biomedical informatics2022

Dynamic assessment of the COVID-19 vaccine acceptance leveraging social media data.

Lingyao Li, Jiayan Zhou, Zihui Ma, Michelle T Bensi, Molly A Hall, Gregory B Baecher

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

6 authors.

Lingyao LiDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. Electronic address: lli12316@umd.edu.
Jiayan ZhouDepartment of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, USA; Department of Statistics, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA. Electronic address: jpz5091@psu.edu.
Zihui MaDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. Electronic address: zma88@terpmail.umd.edu.
Michelle T BensiDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. Electronic address: mbensi@umd.edu.
Molly A HallDepartment of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, USA; The Huck Institutes of the Life Science, The Pennsylvania State University, University Park, PA, USA; Penn State Cancer Institute, The Pennsylvania State University, University Park, PA, USA. Electronic address: mah546@psu.edu.
Gregory B BaecherDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA. Electronic address: gbaecher@umd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccination is the most effective way to provide long-lasting immunity against viral infection; thus, rapid assessment of vaccine acceptance is a pressing challenge for health authorities. Prior studies have applied survey techniques to investigate vaccine acceptance, but these may be slow and expensive. This study investigates 29 million vaccine-related tweets from August 8, 2020 to April 19, 2021 and proposes a social media-based approach that derives a vaccine acceptance index (VAI) to quantify Twitter users' opinions on COVID-19 vaccination. This index is calculated based on opinion classifications identified with the aid of natural language processing techniques and provides a quantitative metric to indicate the level of vaccine acceptance across different geographic scales in the U.S. The VAI is easily calculated from the number of positive and negative Tweets posted by a specific users and groups of users, it can be compiled for regions such a counties or states to provide geospatial information, and it can be tracked over time to assess changes in vaccine acceptance as related to trends in the media and politics. At the national level, it showed that the VAI moved from negative to positive in 2020 and maintained steady after January 2021. Through exploratory analysis of state- and county-level data, reliable assessments of VAI against subsequent vaccination rates could be made for counties with at least 30 users. The paper discusses information characteristics that enable consistent estimation of VAI. The findings support the use of social media to understand opinions and to offer a timely and cost-effective way to assess vaccine acceptance.

Indexed as

COVID-19Social MediaCOVID-19 VaccinesHumansNatural Language ProcessingVaccinationCOVID-19 VaccinesCOVID-19 vaccineNatural language processingSocial mediaText classificationVaccine acceptance

Identifiers

PMID35331966
PMCPMC8935963

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.