Evidence map›Paper›PMID 33481756›Full record

ArticleJournal of medical Internet research2021

Factors Driving Citizen Engagement With Government TikTok Accounts During the COVID-19 Pandemic: Model Development and Analysis.

Qiang Chen, Chen Min, Wei Zhang, Xiaoyue Ma, Richard Evans

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 3 pooled it
48.8field-weighted citation impact, top 1% of its field
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

44 citing papers in PubMed, 3 syntheses or guidelines pooled it, 164 citations in OpenAlex.

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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 at 4 institutions in 3 countries.

Qiang ChenSchool of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.ORCID 0000-0001-8123-077X
Chen MinDepartment of Media and Communication, City University of Hong Kong, Hong Kong, China.ORCID 0000-0001-8770-3398
Wei ZhangSchool of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0003-0178-0750
Xiaoyue MaSchool of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.ORCID 0000-0003-4932-6450
Richard EvansCollege of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom.ORCID 0000-0001-6367-0560
Xi'an Jiaotong University · CNBrunel University of London · GBCity University of Hong Kong · HKHuazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, growth in citizen engagement with social media platforms has enabled public health departments to accelerate and improve health information dissemination, developing transparency and trust between governments and citizens. In light of these benefits, it is imperative to learn the antecedents and underlying mechanisms for this to maintain and enhance engagement.

objectiveThe aim of this study is to determine the factors and influencing mechanisms related to citizen engagement with the TikTok account of the National Health Commission of China during the COVID-19 pandemic.

methodsUsing a web crawler, 355 short videos were collected from the Healthy China account on TikTok (with more than 3 million followers throughout China), covering the period from January 21, 2020, to April 25, 2020. The title and video length, as well as the number of likes, shares, and comments were collected for each video. After classifying them using content analysis, a series of negative binomial regression analyses were completed.

resultsAmong the 355 videos, 154 (43.4%) related to guidance for clinicians, patients, and ordinary citizens, followed by information concerning the government's handling of the pandemic (n=100, 28.2%), the latest news about COVID-19 (n=61, 17.2%), and appreciation toward frontline emergency services (n=40, 11.3%). Video length, titles, dialogic loop, and content type all influenced the level of citizen engagement. Specifically, video length was negatively associated with the number of likes (incidence rate ratio [IRR]=0.19, P<.001) and comments (IRR=0.39, P<.001). Title length was positively related to the number of shares (IRR=24.25, P=.01), likes (IRR=8.50, P=.03), and comments (IRR=7.85, P=.02). Dialogic loop negatively predicted the number of shares (IRR=0.56, P=.03). In comparison to appreciative information, information about the government's handling of the situation (IRR=5.16, P<.001) and guidelines information (IRR=7.31, P<.001) were positively correlated with the number of shares, while the latest news was negatively related to the number of likes received (IRR=0.46, P=.004). More importantly, the relationship between predictors and citizen engagement was moderated by the emotional valence of video titles. Longer videos with positive titles received a higher number of likes (IRR=21.72, P=.04) and comments (IRR=10.14, P=.047). Furthermore, for short videos related to government handling of the pandemic (IRR=14.48, P=.04) and guidance for stakeholders (IRR=7.59, P=.04), positive titles received a greater number of shares. Videos related to the latest news (IRR=66.69, P=.04) received more likes if the video title displayed higher levels of positive emotion.

conclusionsDuring the COVID-19 pandemic, videos were frequently published on government social media platforms. Video length, title, dialogic loop, and content type significantly influenced the level of citizen engagement. These relationships were moderated by the emotional valence of the video's title. Our findings have implications for maintaining and enhancing citizen engagement via government social media.

Indexed as

COVID-19EmotionsFederal GovernmentInformation DisseminationPandemicsChinaHealth EducationHumansPublic Health AdministrationSocial MediaVideo Recordingcitizen engagementCOVID-19dialogic loopemotion valencegovernment social mediapublic health crisisTikTok

Identifiers

PMID33481756
PMCPMC7864626
OpenAlexW3123112026

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.