Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28749, first published .
Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk

Journals

  1. 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
  2. Benjamin V, Raghu T. Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research 2023;34(2):487 View
  3. Suyal H, Singh A. A Systematic Literature Survey of Crowdsourcing: Current Status and Future Perspectives. WIREs Data Mining and Knowledge Discovery 2025;15(3) View
  4. Kate R, Mukherjee A, Bhattacharya J. The Synurbisation Challenge in India: A Review of Ecological Gaps and AI-Driven Monitoring Opportunities. Mineral Metal Energy Oil Gas and Aggregate 2025:358 View

Books/Policy Documents

  1. Noaeen M, Doiron D, Syer J, Brook J. Principles and Advances in Population Neuroscience. View
  2. Schneider J, Eisenhardt D, Utama C, Meske C. Solutions and Technologies for Responsible Digitalization. View

Conference Proceedings

  1. Kazari K, Chen Y, Shakeri Z. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy View