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

Formative Evaluation of Digital Health Interventions (2491) Natural Language Processing (706)

Published on in Vol 4, No 1 (2020): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13296, first published .
Using Natural Language Processing to Examine the Uptake, Content, and Readability of Media Coverage of a Pan-Canadian Drug Safety Research Project: Cross-Sectional Observational Study

Using Natural Language Processing to Examine the Uptake, Content, and Readability of Media Coverage of a Pan-Canadian Drug Safety Research Project: Cross-Sectional Observational Study

Using Natural Language Processing to Examine the Uptake, Content, and Readability of Media Coverage of a Pan-Canadian Drug Safety Research Project: Cross-Sectional Observational Study

Authors of this article:

Hossein Mohammadhassanzadeh1 Author Orcid Image ;   Ingrid Sketris1 Author Orcid Image ;   Robyn Traynor1 Author Orcid Image ;   Susan Alexander2 Author Orcid Image ;   Brandace Winquist3 Author Orcid Image ;   Samuel Alan Stewart1 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Abdalla M, Chen B, Santiago R, Young J, Eder L, Chan A, Pope E, Tu K, Jaakkimainen L, Drucker A. Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases. Journal of Investigative Dermatology 2021;141(7):1840 View
  2. Shakeri Hossein Abad Z, Butler G, Thompson W, Lee J. Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research. JMIR Public Health and Surveillance 2022;8(2):e32355 View