Automatic detection of tweets reporting cases of influenza like illnesses in Australia
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Khanna, Sankalp
Nguyen, Anthony
Boyle, Justin
Hamlet, Matthew
Cameron, Mark
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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.
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Health Information Science and Systems
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3
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1
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© 2015 Zuccon et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Science & Technology
Life Sciences & Biomedicine
Medical Informatics
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Zuccon, G; Khanna, S; Nguyen, A; Boyle, J; Hamlet, M; Cameron, M, Automatic detection of tweets reporting cases of influenza like illnesses in Australia, Health Information Science and Systems, 2015, 3 (1), pp. S4