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Automatic detection of tweets reporting cases of influenza like illnesses in Australia

机译:在澳大利亚自动检测报告流感样疾病的推文报告

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摘要

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.
机译:及早发现疾病暴发对控制和管理疾病传播至关重要。在这项工作中,我们调查了统计机器学习方法是否适合自动检测可能报告类似疾病(ILI)等流感病例的Twitter消息(推文)。对来自澳大利亚维多利亚州的大量推文在3.5个月内获得的经验结果表明,机器学习分类器可以有效地识别提及ILI可能情况的推文(最高0.736 F值,即谐波精确度和查全率的平均值),而与研究中研究的分类器采用的具体技术无关。

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