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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

  • Published:2023-12-15 Issue: Volume:63 Page:5250-5259
  • ISSN:
  • Container-title:2023 IEEE International Conference on Big Data (BigData)
  • language:
  • Short-container-title:

Author:

Hu Ruofan1,Zhang Dongyu1,Tao Dandan2,Zhang Huayi3,Feng Hao4,Rundensteiner Elke5

Affiliation:

1. Worcester Polytechnic Institute,Data Science Program,Worcester,USA

2. Tsinghua University,Vanke School of Public Health,Beijing,China

3. ByteDance,San Jose,USA

4. North Carolina A&T State University,College of Ag&Environ Sciences,Greensboro,USA

5. Worcester Polytechnic Institute,Computer Science/Data Science Program,Worcester,USA

Funder

Worcester Polytechnic Institute

Publisher

IEEE

Reference47 articles.

1. Acute Complications and Sequelae from Foodborne Infections: Informing Priorities for Cost of Foodborne Illness Estimates

2. The Economic Burden of Foodborne Illness in the United States

3. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—new york city, 2012–2013;Harrison,2014

4. Health department use of social media to identify foodborne illness - chicago, illinois, 2013-2014;Harris,2014

5. Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media

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