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

Infodemiology and Infoveillance (1369) New Methods (964) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050)

Published on in Vol 21, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12881, first published .
Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Authors of this article:

Zubair Shah1 Author Orcid Image ;   Paige Martin1 Author Orcid Image ;   Enrico Coiera1 Author Orcid Image ;   Kenneth D Mandl2, 3 Author Orcid Image ;   Adam G Dunn1 Author Orcid Image
Article Authors Cited by (16) Tweetations (18) Metrics

Zubair Shah   1 , PhD ;   Paige Martin   1 , BE ;   Enrico Coiera   1 , PhD, MBBS ;   Kenneth D Mandl   2, 3 , MD, MPH ;   Adam G Dunn   1 , PhD

1 Centre for Health Informatics, Australian Institute for Health Innovation, Macquarie University, Sydney, Australia

2 Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States

3 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States

Corresponding Author:

  • Zubair Shah, PhD
  • Centre for Health Informatics
  • Australian Institute for Health Innovation
  • Macquarie University
  • 75 Talavera Road, Macquarie Park
  • Sydney, 2113
  • Australia
  • Phone: 61 404941319
  • Email: zubair.shah@mq.edu.au