Whose tweets on COVID-19 gain the most attention: celebrities, politcal or scientific authorities?

Background Twitter has considerable capacity for health education and proves to be an efficient and popular communication tool in the COVID-19 pandemic. Although a number of stakeholders saturate Twitter with COVID-19-related information it remains unknown who disseminates information most efficaciously. Methods COVID-19-related tweets were obtained from Twitter accounts of health agencies, governmental authorities, universities, scientific journals, medical associations, and celebrities. The impact of posts was measured with the nominal and relative (%followers) number of likes and retweets. Sentiment analysis was conducted. Results We have identified 17,331 COVID-19-related tweets posted by 338 accounts in over four months since the virus began to spread. The largest number of likes was received by tweets of celebrities (median nominal, relative likes; 14,918, 0.036%), politicians (259, 0.174%), and health agencies (231, 0.007%). Most retweeted messages were also posted by celebrities (2,366, 0.005%), health agencies (130, 0.004%), and politicians (55, 0.041%). Retweets and likes peaked in March 2020 and the overall sentiment of the tweets was growing steadily. Whereas celebrities and politicians posted positive messages, the scientific and health authorities more often employed a negative vocabulary. The posts with positive sentiment gained more likes, and relative likes than non-positive. Conclusions During the pandemic, the tweets of celebrities, and politicians related to COVID-19 outperform those coming from health and scientific institutions. Active engagement of Twitter influencers may help key messages go viral.

Identifier
DOI https://doi.org/10.17632/nn442rvb8j.2
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-4l-wasl
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:180053
Provenance
Creator Kamiński, M
Publisher Data Archiving and Networked Services (DANS)
Contributor Mikołaj Kamiński
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/licenses/by/4.0; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Other