Misinformation warnings: Twitter's soft moderation effects on COVID-19 vaccine belief echoes.

Belief echoes COVID-19 Contextual tags Interstitial covers Misinformation Soft moderation Twitter Warnings

Journal

Computers & security
ISSN: 0167-4048
Titre abrégé: Comput Secur
Pays: England
ID NLM: 9881584

Informations de publication

Date de publication:
Mar 2022
Historique:
received: 28 08 2021
revised: 17 11 2021
accepted: 13 12 2021
pubmed: 23 12 2021
medline: 23 12 2021
entrez: 22 12 2021
Statut: ppublish

Résumé

Twitter, prompted by the rapid spread of alternative narratives, started actively warning users about the spread of COVID-19 misinformation. This form of soft moderation comes in two forms: as an interstitial cover before the Tweet is displayed to the user or as a contextual tag displayed below the Tweet. We conducted a 319-participants study with both verified and misleading Tweets covered or tagged with the COVID-19 misinformation warnings to investigate how Twitter users perceive the accuracy of COVID-19 vaccine content on Twitter. The results suggest that the interstitial covers work, but not the contextual tags, in reducing the perceived accuracy of COVID-19 misinformation. Soft moderation is known to create so-called "belief echoes" where the warnings echo back, instead of dispelling, preexisting beliefs about morally-charged topics. We found that such "belief echoes" do exist among Twitter users in relationship to the perceived safety and efficacy of the COVID-19 vaccine as well as the vaccination hesitancy for themselves and their children. These "belief echoes" manifested as skepticism of adequate COVID-19 immunization particularly among Republicans and Independents as well as female Twitter users. Surprisingly, we found that the belief echoes are strong enough to preclude adult Twitter users to receive the COVID-19 vaccine regardless of their education level.

Identifiants

pubmed: 34934255
doi: 10.1016/j.cose.2021.102577
pii: S0167-4048(21)00401-6
pmc: PMC8675217
doi:

Types de publication

Journal Article

Langues

eng

Pagination

102577

Informations de copyright

© 2021 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Filipo Sharevski (F)

College of Computing and Digital Media, DePaul University, 243 S Wabash Ave, Chicago, IL 60640, United States.

Raniem Alsaadi (R)

College of Computing and Digital Media, DePaul University, 243 S Wabash Ave, Chicago, IL 60640, United States.

Peter Jachim (P)

College of Computing and Digital Media, DePaul University, 243 S Wabash Ave, Chicago, IL 60640, United States.

Emma Pieroni (E)

College of Computing and Digital Media, DePaul University, 243 S Wabash Ave, Chicago, IL 60640, United States.

Classifications MeSH