Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46484, first published .
Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort

Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort

Automatically Identifying Self-Reports of COVID-19 Diagnosis on Twitter: An Annotated Data Set, Deep Neural Network Classifiers, and a Large-Scale Cohort

Journals

  1. Almasoud A, Alshahrani H, Hassan A, Almalki N, Motwakel A. Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets. Electronics 2023;12(19):4125 View
  2. Klein A, Banda J, Guo Y, Schmidt A, Xu D, Flores Amaro I, Rodriguez-Esteban R, Sarker A, Gonzalez-Hernandez G. Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium. Journal of the American Medical Informatics Association 2024;31(4):991 View
  3. Xie J, Zhang Z, Zeng S, Hilliard J, An G, Tang X, Jiang L, Yu Y, Wan X, Xu D. Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis. Journal of Medical Internet Research 2025;27:e63190 View

Books/Policy Documents

  1. Jiang Y, Qiu R, Zhang Y, Zhang P. Databases Theory and Applications. View