Public Opinions toward COVID-19 Vaccine Mandates: A Machine Learning-based Analysis of U.S. Tweets.

Yawen Guo, Jun Zhu, Yicong Huang, Lu He, Changyang He, Chen Li, Kai Zheng

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 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

–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

2 citing papers in PubMed.

A thematic analysis of UK COVID-19 vaccine hesitancy discussions on Twitter.BMC public health · 2025
Article
Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data.Journal of medical Internet research · 2024
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

7 authors.

Yawen GuoUniversity of California, Irvine, Irvine, CA, USA.

Jun ZhuUniversity of California, Irvine, Irvine, CA, USA.

Yicong HuangUniversity of California, Irvine, Irvine, CA, USA.

Lu HeUniversity of California, Irvine, Irvine, CA, USA.

Changyang HeHong Kong University of Science and Technology, Hong Kong SAR, China.

Chen LiUniversity of California, Irvine, Irvine, CA, USA.

Kai ZhengUniversity of California, Irvine, Irvine, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While it has been scientifically proven that COVID-19 vaccine is a safe and effective measure to reduce the severity of infection and curbing the spread of the SARS-CoV-2 virus, skepticism remains widespread, and in many countries vaccine mandates have been met with strong opposition. In this study, we applied machine learning-based analyses of the U.S.-based tweets covering the periods leading toward and after the Biden Administration's announcement of federal vaccine mandates, supplemented by a qualitative content analysis of a random sample of relevant tweets. The objective was to examine the beliefs held among twitter users toward vaccine mandates, as well as the evidence that they used to support their positions. The results show that while approximately 30% of the twitter users included in the dataset supported the measure, more users expressed differing opinions. Concerns raised included questioning on the political motive, infringement of personal liberties, and ineffectiveness in preventing infection.

Indexed as

COVID-19Social MediaCOVID-19 VaccinesHumansMachine LearningPublic OpinionSARS-CoV-2COVID-19 VaccinesCOVID-19 vaccine mandate policy; sentiment analysispublic opinionTwitter

Identifiers

PMID37128441

PMCPMC10148373

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.