Regional Level Influenza Study with Geo-Tagged Twitter Data
Regional Level Influenza Study with Geo-Tagged Twitter Data
复制标题
DOI：
10.1007/s10916-016-0545-y
复制
发表时间：
2016-08-01
影响因子：
5.3
通讯作者：
Debruyn, Anton
中科院分区：
文献类型：
--
作者：
Wang, Feng;Wang, Haiyan;Debruyn, Anton
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。我们调查了Twitter流感计数与疾病控制和预防中心（CDC）官方统计数据之间的相关性，发现实时推文流可以捕捉国家和地区层面的流感病例动态，并可能作为流感流行的早期预警系统。此外，我们提出了一个动态的数学模型，可以预测Twitter流感计数与高精度。
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. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.