Piecing together the narrative of #longcovid: an unsupervised deep learning of 1,354,889 X (formerly Twitter) posts from 2020 to 2023.

Journal: Frontiers in public health
PMID: 39737451

Abstract

OBJECTIVE: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.

Authors

  • Qin Xiang Ng
    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
  • Liang En Wee
    Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.
  • Yu Liang Lim
    Department of General Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
  • Rebecca Hui Shan Ong
    Health Services Research, Changi General Hospital, Singapore, Singapore.
  • Clarence Ong
    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
  • Indumathi Venkatachalam
    Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.
  • Tau Ming Liew
    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.

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