Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

Journal: Journal of medical Internet research
Published Date: Aug 12, 2020

Abstract

BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets.

Authors

  • Shyam Visweswaran
    University of Pittsburgh, Pittsburgh, PA, USA.
  • Jason B Colditz
    School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
  • Patrick O'Halloran
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Na-Rae Han
    Department of Linguistics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Sanya B Taneja
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.
  • Joel Welling
    Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, United States.
  • Kar-Hai Chu
    School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
  • Jaime E Sidani
    School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
  • Brian A Primack
    College of Education and Health Professions, University of Arkansas, Fayetteville, AR, United States.

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