Evidence map›Paper›PMID 36465093›Full record

ArticleSN social sciences2022

To wear or not to wear? Unpacking the #NoMask discourses and conversations on Twitter.

Anita Lavorgna, Les Carr, Ashton Kingdon

Abstract read
In one paragraph

Article in SN social sciences, 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

3 authors.

Anita LavorgnaDepartment of Sociology, Social Policy and Criminology, University of Southampton, Southampton, UK.ORCID 0000-0001-8484-1613
Les CarrWeb Science Institute, University of Southampton, Southampton, UK.
Ashton KingdonDepartment of Sociology, Social Policy and Criminology, University of Southampton, Southampton, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of the COVID-19 health crisis, the use of face masks has been a topic broadly debated. In many Western countries, especially at the heights of the pandemic, discussions on the use of protective facemasks were often linked to what were mainly political considerations, often fueled by health-related misinformation. Our study brings together social sciences and computer science expertise to retrospectively unpack the #NoMask discourses and conversations using both network analysis approaches on big data retrieved from Twitter and qualitative analyses on sub-sets of relevant social media data. By looking comparatively at two dataset gathered at different stages of the health crisis (2020 and 2022), we aim to better understand the role of Twitter in that interesting area where the dissemination of health misinformation became capitalized by the political narrative linking the social discontent caused by the socio-economic impacts of the pandemic to specific political ideologies. Our analyses show that there has never been a unique 'NoMask movement,' nor a defined online community. Rather, we can identify a range of relatively niche, loosely connected, and heterogeneous actors that, in the course of the pandemic, independently pushed diverse (but converging and compatible) discourses. Conversations directly linked to the #NoMask relevant hashtags are overall limited, as twitters using them are not talking to each other; nonetheless, they successfully engaged a larger audience.

Indexed as

COVID-19MasksMedical misinformationSocial media analysisTwitter

Identifiers

PMID36465093
PMCPMC9685024

What OpenQuestion holds

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LicenceCC BY
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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.