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Title: Wording Matters: the Effect of Linguistic Characteristics and Political Ideology on Resharing of COVID-19 Vaccine Tweets

Social media platforms are frequently used to share information and opinions around vaccinations. The more often a message is reshared, the wider the reach of the message and potential influence it may have on shaping people’s opinions to get vaccinated or not. We used a negative binomial regression to investigate whether a message’s linguistic characteristics (degree of concreteness, emotional arousal, and sentiment) and user characteristics (political ideology and number of followers) may influence users’ decisions to reshare tweets related to the COVID-19 vaccine. We analyzed US English-language tweets related to the COVID-19 vaccine between May 2020 and October 2021 (N = 236,054).

Tweets with positive and high-arousal words were more often retweeted than negative, low-arousal tweets. Tweets with abstract words were more often retweeted than tweets with concrete words. In addition, while Liberal users were more likely to have tweets with a positive sentiment reshared, Conservative users were more likely to have tweets with a negative sentiment reshared. Our results can inform public health messaging on how to best phrase vaccine information to impact engagement and information resharing, and potentially persuade a wider set of people to get vaccinated.

 
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Award ID(s):
2107150
PAR ID:
10542640
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
ACM Transactions on Computer-Human Interaction
ISSN:
1073-0516
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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