A novel machine learning framework for comparison of viral COVID-19–related Sina Weibo and Twitter posts : workflow development and content analysis

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Chen, S., Zhou, L., Song, Y., Xu, Q., Wang, P., Wang, K., … Janies, D. (2021). A novel machine learning framework for comparison of viral COVID-19–related Sina Weibo and Twitter posts : workflow development and content analysis. Journal Of Medical Internet Research. https://doi.org/doi:10.2196/24889
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Description

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.

Details
Title

A novel machine learning framework for comparison of viral COVID-19–related Sina Weibo and Twitter posts : workflow development and content analysis

Author(s)

Chen, Shi
Zhou, Lina
Song, Yunya
Xu, Qian
Wang, Ping
Wang, Kanlun
Ge, Yaorong
Janies, Daniel

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Published

2021

Date Created

2021

Material Type

Journal article

Genre

articles

Resource Type

text

Abstract

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.

Subjects--Topics

COVID-19 (Disease)
Twitter (Firm)
Social media
Machine learning

Is Part Of

Journal of medical Internet research

Volume

Vol. 23, issue 1

Language

English

Published Version (Please cite this version)

doi:10.2196/24889

Publisher's Version

https://www.jmir.org/2021/1/e24889/

Permalink

http://hdl.handle.net/20.500.13093/work:208

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