Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Jun 15, 2020)
Date Submitted: Feb 22, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Effect of Health Information Dissemination on Users’ Following and Clicking a Like During Novel Coronavirus Outbreak in China: Data and Content Analysis
ABSTRACT
Background:
In December 2019, novel coronavirus broke out in Wuhan, Hubei with an impact spreading to the whole of China. Under this condition, many WeChat official accounts have posted articles daily to transmit health information about the epidemic. We found that the number of followers in many official accounts has soared, whereas that in several accounts has remained stable or decreased. Moreover, the numbers and classifications of essays posted have varied amongst accounts.
Objective:
This study aims to explore the impact factors of health information dissemination on users’ behaviours in WeChat.
Methods:
We adopted the uses and gratifications theory to reveal the principle in information dissemination of the official accounts. Two-wave data, comprising the number of followers from the top 200 official accounts on 21 January 2020 and 27 January 2020, were used to calculate the increase. We selected them during the seven-day period as the first dependent variable. The total number of likes from headlines on the epidemic in this period was selected as the second dependent variable. The number of each type of articles and headlines about the coronavirus served as the independent variables. The above data were used to develop multiple and simple linear regression models. We used content analysis to explore other factors affecting users’ behaviour.
Results:
The top 200 official accounts can be classified to institution, medicine and individual groups. For institution and medicine groups, the adjusted R2 value in the multiple linear regression model were 0.355 and 0.452, respectively. For the institution group, the adjusted R2 value in the simple linear regression model was 0.317. The other results were insignificant, and we could not develop an ideal model for them. However, the above R2 value indicated a good fit. For the institution group, report and story types of articles were significant for the multiple linear regression model (B=2.724, P=.007; B=14.875, P=.003), and both were identified positive effects. For the simple linear regression model, the number of headlines on coronavirus was identified positive effect (B=3.084, P<.001). For the medicine group, report and science types were significant for the multiple linear regression model (B=4.381, P=.009; B=31.564, P<.001) and had a positive effect.
Conclusions:
Different factors in health information dissemination contribute to users’ behaviour. Through content analysis, we concluded that articles with multiform information and certain types are considerably more popular than their counterpart.
Citation
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