TY  - JOUR
AU  - Chen, Shi
AU  - Zhou, Lina
AU  - Song, Yunya
AU  - Xu, Qian
AU  - Wang, Ping
AU  - Wang, Kanlun
AU  - Ge, Yaorong
AU  - Janies, Daniel
PY  - 2021
DA  - 2021/1/6
TI  - A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis
JO  - J Med Internet Res
SP  - e24889
VL  - 23
IS  - 1
KW  - COVID-19
KW  - Twitter
KW  - Sina Weibo
KW  - content feature extraction
KW  - cross-cultural comparison
KW  - machine learning
KW  - social media
KW  - infodemiology
KW  - infoveillance
KW  - content analysis
KW  - workflow
KW  - communication
KW  - framework
AB  - Background: Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a 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 sociocultural spectrum. Objective: We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods: We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics 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 web-based health communications. Results: There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions: We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/1/e24889
UR  - https://doi.org/10.2196/24889
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33326408
DO  - 10.2196/24889
ID  - info:doi/10.2196/24889
ER  -