A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.

Journal: Journal of medical Internet research
Published Date: Jan 6, 2021

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.

Authors

  • Shi Chen
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.
  • Lina Zhou
    Department of Business Information Systems and Operations Management, The University of North Carolina at Charlotte, Charlotte, NC USA.
  • Yunya Song
    Department of Journalism, Hong Kong Baptist University, Hong Kong, Hong Kong.
  • Qian Xu
    College of Information Science and Engineering, Hunan Normal University, Changsha, P.R. China.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Kanlun Wang
    School of Business, University of North Carolina at Charlotte, Charlotte, NC, United States.
  • Yaorong Ge
    Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States.
  • Daniel Janies
    Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States.

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