title: Automatic detection of tweets reporting cases of influenza like illnesses in Australia.
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.
authors: Information Systems School, Queensland University of Technology, Brisbane, Australia ; The Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Brisbane, Australia.
authors: Guido
authors: G
authors: Zuccon
authors: The Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Brisbane, Australia.
authors: Sankalp
authors: S
authors: Khanna
authors: The Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Brisbane, Australia.
authors: Anthony
authors: A
authors: Nguyen
authors: The Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Brisbane, Australia.
authors: Justin
authors: J
authors: Boyle
authors: The Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Brisbane, Australia.
authors: Matthew
authors: M
authors: Hamlet
authors: CSIRO Digital Productivity Flagship, Canberra, Australia.
authors: Mark
authors: M
authors: Cameron