Evidence map›Paper›PMID 36896130›Full record

ArticlePNAS nexus2023

Understanding underlying moral values and language use of COVID-19 vaccine attitudes on twitter.

Judith Borghouts, Yicong Huang, Sydney Gibbs, Suellen Hopfer, Chen Li, Gloria Mark

Abstract read
In one paragraph

Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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.

Judith BorghoutsDepartment of Medicine, University of California Irvine, Irvine, CA 92617, USA.ORCID https://orcid.org/https://orcid.org/0000-0001-9716-0147
Yicong HuangDepartment of Computer Science, University of California Irvine, Irvine, CA 92697, USA.
Sydney GibbsDepartment of Computer Science, University of California Irvine, Irvine, CA 92697, USA.
Suellen HopferDepartment of Health, Society & Behavior in the Program in Public Health, University of California Irvine, Irvine, CA 92697, USA.
Chen LiDepartment of Computer Science, University of California Irvine, Irvine, CA 92697, USA.
Gloria MarkDepartment of Computer Science, University of California Irvine, Irvine, CA 92697, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public sentiment toward the COVID-19 vaccine as expressed on social media can interfere with communication by public health agencies on the importance of getting vaccinated. We investigated Twitter data to understand differences in sentiment, moral values, and language use between political ideologies on the COVID-19 vaccine. We estimated political ideology, conducted a sentiment analysis, and guided by the tenets of moral foundations theory (MFT), we analyzed 262,267 English language tweets from the United States containing COVID-19 vaccine-related keywords between May 2020 and October 2021. We applied the Moral Foundations Dictionary and used topic modeling and Word2Vec to understand moral values and the context of words central to the discussion of the vaccine debate. A quadratic trend showed that extreme ideologies of both Liberals and Conservatives expressed a higher negative sentiment than Moderates, with Conservatives expressing more negative sentiment than Liberals. Compared to Conservative tweets, we found the expression of Liberal tweets to be rooted in a wider set of moral values, associated with moral foundations of care (getting the vaccine for protection), fairness (having access to the vaccine), liberty (related to the vaccine mandate), and authority (trusting the vaccine mandate imposed by the government). Conservative tweets were found to be associated with harm (around safety of the vaccine) and oppression (around the government mandate). Furthermore, political ideology was associated with the expression of different meanings for the same words, e.g. "science" and "death." Our results inform public health outreach communication strategies to best tailor vaccine information to different groups.

Indexed as

COVID-19moral foundations theorypolitical ideologysentimentsocial mediavaccination

Identifiers

PMID36896130
PMCPMC9991494

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.