Automatic detection of tweets reporting cases of influenza like illnesses in Australia. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Zuccon, Guido; Khanna, Sankalp; Nguyen, Anthony; Boyle, Justin; Hamlet, Matthew; Cameron, Mark
• Is Part Of: Health information science and systems. Volume 3:Issue 1(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; Medical Informatics -- Periodicals; Medical informatics; Medicine -- Data processing; Periodicals; Electronic journals; 610.28
• DOI: 10.1186/2047-2501-3-S1-S4
• Languages: English
• ISSNs: 2047-2501

Abstract

Abstract Early detection of disease outbreaks is critical for disease spread control and management. In this work we investigate the suitability of statistical machine learning approaches to automatically detect Twitter messages (tweets) that are likely to report cases of possible influenza like illnesses (ILI). Empirical results obtained on a large set of tweets originating from the state of Victoria, Australia, in a 3.5 month period show evidence that machine learning classifiers are effective in identifying tweets that mention possible cases of ILI (up to 0.736 F-measure, i.e. the harmonic mean of precision and recall), regardless of the specific technique implemented by the classifier investigated in the study.