Modeling Flu Trends with Real-Time Geo-tagged Twitter Data Streams
Modeling Flu Trends with Real-Time Geo-tagged Twitter Data Streams
复制标题
使用实时地理标记的 Twitter 数据流对流感趋势进行建模
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Feng Wang
中科院分区：
文献类型：
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作者：
Jaime Chon;R. Raymond;Haiyan Wang;Feng Wang
数以百万计的用户在社交媒体上生成和阅读的丰富数据以快速准确的方式讲述了真实的世界正在发生的事情。近年来，许多研究人员已经探索了Twitter的实时流数据的广泛应用，包括预测股票市场和公共卫生趋势。在本文中，我们设计，实现和评估一个原型系统，收集和分析流感状态在不同的地理位置与实时鸣叫流。为了评估基于推文流的流感估计的准确性，我们将结果与疾病控制和预防中心（CDC）的官方统计数据相关联。我们的初步结果表明，实时推文流可以捕捉到国家层面的流感动态，并有可能作为流感流行或流感趋势的预警系统。
The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. To evaluate the accuracy of the influenza estimation based on tweet streams, we correlate the results with official statistics from Center for Disease Control and Prevention (CDC). Our preliminary results have demonstrated that real-time tweet streams capture the dynamics of influenza at national level, and could potentially serve as an early warning system of influenza epidemics or flu trends.
DOI：
10.4269/ajtmh.2012.11-0597
发表时间：
2012-01-01
影响因子：
3.3
作者：
Chunara, Rumi;Andrews, Jason R.;Brownstein, John S.
通讯作者：
Brownstein, John S.