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](https://asset.jmir.pub/assets/c67a3a8be18577b28c70cf7193a6b8f1.png 480w,https://asset.jmir.pub/assets/c67a3a8be18577b28c70cf7193a6b8f1.png 960w,https://asset.jmir.pub/assets/c67a3a8be18577b28c70cf7193a6b8f1.png 1920w,https://asset.jmir.pub/assets/c67a3a8be18577b28c70cf7193a6b8f1.png 2500w)
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