Automatic online news monitoring and classification for syndromic surveillance

Yulei Zhang, Yan Dang, Hsinchun Chen, Mark Thurmond, Cathy Larson

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Syndromic surveillance can play an important role in protecting the public's health against infectious diseases. Infectious disease outbreaks can have a devastating effect on society as well as the economy, and global awareness is therefore critical to protecting against major outbreaks. By monitoring online news sources and developing an accurate news classification system for syndromic surveillance, public health personnel can be apprised of outbreaks and potential outbreak situations. In this study, we have developed a framework for automatic online news monitoring and classification for syndromic surveillance. The framework is unique and none of the techniques adopted in this study have been previously used in the context of syndromic surveillance on infectious diseases. In recent classification experiments, we compared the performance of different feature subsets on different machine learning algorithms. The results showed that the combined feature subsets including Bag of Words, Noun Phrases, and Named Entities features outperformed the Bag of Words feature subsets. Furthermore, feature selection improved the performance of feature subsets in online news classification. The highest classification performance was achieved when using SVM upon the selected combination feature subset.

Original languageEnglish (US)
Pages (from-to)508-517
Number of pages10
JournalDecision Support Systems
Issue number4
StatePublished - Nov 2009


  • Feature selection
  • News classification
  • News monitoring
  • Syndromic surveillance

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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