Social media crisis communication and public engagement during COVID-19 analyzing public health and news media organizations' tweeting strategies.

Ting Song, Ping Yu, Brian Yecies, Jiang Ke, Haiyan Yu

Abstract read

In one paragraph

Article in Scientific reports, 2025. 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

5 authors.

Ting SongCentre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, 2522, Australia.

Ping YuCentre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, 2522, Australia. ping@uow.edu.au.

Brian YeciesCommunication and Media, School of the Arts, English and Media, Faculty of Law, Humanities and the Arts, University of Wollongong, Wollongong, 2500, Australia.

Jiang KeCentre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, 2522, Australia.

Haiyan YuSchool of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates early COVID-19 communication strategies and content of four public health and four news media organizations across Australia, China, the UK, and the US on X (formerly Twitter) and their public engagement. 15,711 COVID-19-related tweets from the selected accounts posted from January 1 to May 19, 2020, were collected using a web crawler. Public engagement was measured through replies, retweets, and likes. The tweets were grouped into 37 clusters using unsupervised learning and analyzed thematically based on the top 30 tweets per cluster. Descriptive statistics quantified the tweets and their engagement, and a chi-square test compared differences between the two organization types across topics. Six topics were identified: "policies, methods, and action", "case updates", "opinions and responses", "medical research and treatment information", "impacts and consequences", and "health instructions and suggestions", with five communication phases: inception, awareness, panic, spreading, and cohabitation. Analysis revealed that more hashtags and longer texts were associated with lower engagement, except for tweets on "medical research and treatment information" and "health instructions and suggestions". Our study indicated that fewer hashtags and concise language might improve public engagement, while detailed content with more hashtags was effective for specific health instructions. Overall, tweets about treatment progress and health guidance received the most engagement.

Indexed as

CommunicationCOVID-19Mass MediaPublic HealthSocial MediaAustraliaChinaHumansInformation DisseminationSARS-CoV-2United KingdomUnited StatesCommunity engagementCoronavirusCrisis communicationDigital communicationHealth communicationInfectious disease outbreaksInformation disseminationPublic health emergency response

Identifiers

PMID40413300

DOI10.1038/s41598-025-90759-w

PMCPMC12103563

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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.