JMIR Publications

ABSTRACT

Background:

Social media play a critical role in health communications especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of universal analytical framework to extract, quantify and compare content features in public discourse of emerging health issues on different social media platforms across a broad socio-cultural spectrum.

Objective:

We aim to develop a novel and universal content feature extraction and analytical framework, and contrast how content features differ with socio-cultural backgrounds in discussions of emerging health crisis on major social media platforms.

Methods:

We sampled 1,000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features in six major categories (e.g., clinical and epidemiological, countermeasures, political and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of online health communications.

Results:

There were substantially different distributions, prevalence, and associations of content features in public discourse about the same COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease and health aspects while Twitter users engaged more about policy, political, and other societal issues.

Conclusions:

We are able to extract a rich set of content features from social media data to accurately characterize public discourse of emerging health issues in different social-cultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other health issues beyond COVID-19.