Attributes and Predictors of Opinion Leaders on Twitter: COVID-19 Childhood Vaccination Campaign

ABSTRACT

Identifying attributes of the COVID-19 childhood vaccine message disseminators is beneficial to health campaign research. This study examines the We Can Do This COVID-19 childhood vaccination health campaign message disseminators on Twitter during 2021–2022. Guided by the diffusion of innovation theory and the theory of the two-step flow communication, we classify the disseminators (n=823) and examine their influence over the two-year period. A manual content analysis and social network analysis were conducted to measure the impact of disseminators on their networks through in-degree centrality assessment. Results revealed that experts in medical fields, industrial professionals, and social media influencers were the top three most common career titles disclosed in Twitter bios among individual accounts, whereas local nonprofit organizations, governments, and companies were the most common organizational accounts. Mann-Whitney U Tests indicated that in both years message disseminators with clinical medicine expertise were more influential than those who did not reveal such expertise. However, only in 2021 were campaign message disseminators with parental expertise more influential within the social network. We discuss the implications of disseminator classification as well as the significance of enhancing health campaigns by identifying who discusses them on social media.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Supplement documents are available in Open Science Framework and can be accessed via https://osf.io/3wq52/?view_only=8daf2c2f00384a01abae91947152d29b.

Notes

1. (child OR kid OR children) AND (vax OR unvax OR vaxed OR vaxxed OR unvaxed OR unvaxxed OR vaccination OR vaccine OR antivax OR provax OR antivaccination OR provaccination OR vaccined OR #vax OR #unvax OR #vaxed OR #unvaxed OR #vaxxed OR #unvaxxed OR #vaccination OR #vaccine OR #antivax OR #provax OR #antivaccination OR #provaccination OR #vaccined) AND (COVID OR COVID-19 OR coronavirus OR #covid OR #covid19 OR #coronavirus OR #longcovid OR #corona OR #CoronaPandemic) NOT (“child psychiatrist” OR “child care” OR “childcare” OR “child abuse” OR “child abuse” OR “child killer” OR #childcarestaff OR “child tax” OR “Golden Child”) AND (#WeCanDoThis).

2. We employed square-root transformations to mitigate data distribution skewness and normalize residuals, following the bulging rule (a strategy for selecting an appropriate λ value in a regression model; Fink, 2009). However, since these transformations do not address the violation of the normality assumption, we retain the original outcomes for non-parametric analysis.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.