Psychological reactance to vaccine mandates on Twitter: a study of sentiments in the United States.

Abstract read

In one paragraph

Article in Journal of public health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026
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

1 author.

Pei-Hsun HsiehCenter for Social Norms and Behavioral Dynamics, University of Pennsylvania, Philadelphia, PA, 19104-6304, USA. phsieh@sas.upenn.edu.ORCID http://orcid.org/0000-0002-1147-8208

Funding

Institute for Humane Studies, George Mason University IHS017468

6 · The paper itself

Abstract

This study examines the relationship between vaccine mandates and public sentiment toward vaccines and health officials on Twitter. I analyzed 6.6 million vaccine-related tweets from July 2021 to February 2022 in the United States. Leveraging a large language model, BERT, I identified tweets discussing vaccine mandates even when lacking explicit keywords. Compared to non-mandate tweets, those mentioning mandates exhibit greater negativity, anger, and freedom-related language. Furthermore, increased state-level discussion of mandates correlates with rising levels of negativity and anger toward both vaccines and public health officials. Finally, greater disparity in vaccination progress across counties within a state is associated with increased anger in tweets directed toward both.

Indexed as

Mandatory ProgramsPublic OpinionSocial MediaVaccinationVaccinesHumansMandatory VaccinationUnited StatesVaccinesNatural language processingPublic health officialsSentiment analysisTwitterVaccine mandates

Identifiers

PMID39828759

DOI10.1057/s41271-025-00554-0

PMCPMC12119348

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.