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

Crowdsourcing and Mechanical Turks (101) Machine Learning (1816) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1098) Infodemiology and Infoveillance (1483) Surveillance Systems (498) Public Health Informatics (106) New Methods (1054) Innovative Methods in Public Health and Surveillance (364)

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

Authors of this article:

Zahra Shakeri Hossein Abad1, 2 Author Orcid Image ;   Gregory P Butler3 Author Orcid Image ;   Wendy Thompson3 Author Orcid Image ;   Joon Lee2, 4, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations (15) Metrics

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

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