Mask images on Twitter increase during COVID-19 mandates, especially in Republican counties.

Xiaofeng Lin, Georgia Kernell, Tim Groeling, Jungseock Joo, Jun Luo, Zachary C Steinert-Threlkeld

Abstract read

In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xiaofeng LinDepartment of Statistics, University of California - Los Angeles, Los Angeles, CA, 90024, USA.

Georgia KernellDepartment of Communication, University of California - Los Angeles, Los Angeles, CA, 90024, USA. gkernell@ucla.edu.

Tim GroelingDepartment of Communication, University of California - Los Angeles, Los Angeles, CA, 90024, USA.

Jungseock JooDepartment of Communication, University of California - Los Angeles, Los Angeles, CA, 90024, USA.

Jun LuoDepartment of Communication, University of California - Los Angeles, Los Angeles, CA, 90024, USA.

Zachary C Steinert-ThrelkeldDepartment of Political Science, University of California - Los Angeles, Los Angeles, CA, 90024, USA.

Funding

Scientific and Technical CoreP2CHD041022 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HIRAM BELTRAN-SANCHEZ · 2016 to 2026

$6.9M

NICHD NIH HHS P2C HD041022

6 · The paper itself

Abstract

Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing-an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises.

Indexed as

COVID-19GovernmentHumansPublic HealthPublic OpinionPublic PolicyUnited States

Identifiers

PMID36494380

DOI10.1038/s41598-022-23368-6

PMCPMC9734103

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.