This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1349) Innovative Methods in Public Health and Surveillance (332) JMIR Theme Issue: COVID-19 Special Issue (2452) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45419, first published .
Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study

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

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

Authors of this article:

Jiageng Wu1, 2 Author Orcid Image ;   Lumin Wang1, 2 Author Orcid Image ;   Yining Hua3, 4 Author Orcid Image ;   Minghui Li1, 2 Author Orcid Image ;   Li Zhou3, 4 Author Orcid Image ;   David W Bates3, 4 Author Orcid Image ;   Jie Yang1, 2 Author Orcid Image
Article Authors Cited by (5) Tweetations (6) Metrics

Journals

  1. Hua Y, Wu J, Lin S, Li M, Zhang Y, Foer D, Wang S, Zhou P, Yang J, Zhou L. Streamlining social media information retrieval for public health research with deep learning. Journal of the American Medical Informatics Association 2024;31(7):1569 View
  2. Kukreti S, Yeh C, Chen Y, Lu M, Li M, Lai Y, Li C, Ko N. Unveiling long COVID symptomatology, co-occurrence trends, and symptom distress post SARS-CoV-2 infection. Journal of Infection and Public Health 2024;17(7):102464 View
  3. Zhang Z, Hua Y, Zhou P, Lin S, Li M, Zhang Y, Zhou L, Liao Y, Yang J. Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing. Health Data Science 2024;4 View
  4. Benito D, Robles J, Ramírez J, Anta A, Aguilar J. An In-Depth Analysis of COVID-19 Symptoms Considering the Co-Occurrence of Symptoms Using Clustering Algorithms. IEEE Access 2024;12:127792 View