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Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.

Jiageng WuLumin WangYining HuaMinghui LiLi ZhouDavid Westfall BatesJie Yang
Published in: Journal of medical Internet research (2023)
This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.
Keyphrases
  • social media
  • coronavirus disease
  • sars cov
  • health information
  • sleep quality
  • machine learning
  • risk assessment
  • respiratory syndrome coronavirus
  • deep learning