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

Public Health Surveillance Practice (165)

Published on in Vol 10, No 1 (2018):

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

Authors of this article:

Katelynn Devinney1 ;   Adile Bekbay1 ;   Thomas Effland2 ;   Luis Gravano2 ;   David Howell1 ;   Daniel Hsu2 ;   Daniel O'Hallorhan1 ;   Vasudha Reddy1 ;   Faina Stavinsky1 ;   HaeNa Waechter1 ;   Bruce Gutelius1
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Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Deng X, Cao S, Horn A. Emerging Applications of Machine Learning in Food Safety. Annual Review of Food Science and Technology 2021;12(1):513 View
  2. Zhou Q, Zhang H, Wang S. Artificial intelligence, big data, and blockchain in food safety. International Journal of Food Engineering 2022;18(1):1 View
  3. Sadilek A, Hswen Y, Bavadekar S, Shekel T, Brownstein J, Gabrilovich E. Lymelight: forecasting Lyme disease risk using web search data. npj Digital Medicine 2020;3(1) View
  4. Siddique A, Gupta A, Sawyer J, Huang T, Morey A. Big data analytics in food industry: a state-of-the-art literature review. npj Science of Food 2025;9(1) View

Books/Policy Documents

  1. Pujahari R, Khan R. Artificial Intelligence Applications in Agriculture and Food Quality Improvement. View

Conference Proceedings

  1. Altenburger K, Ho D. The World Wide Web Conference. Is Yelp Actually Cleaning Up the Restaurant Industry? A Re-Analysis on the Relative Usefulness of Consumer Reviews View