支持对微博中与健康相关的事件进行时间分析

Nattiya Kanhabua, Sara Romano, Avare Stewart, W. Nejdl
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引用次数: 24

摘要

微博服务,如Twitter,作为在社交网络中分享信息的一种方式,正获得越来越多的兴趣。许多研究表明,利用Twitter帖子(或tweets)来推断现实世界事件的存在和规模是有潜力的。在医疗领域,检测与公共卫生有关的推文以进行早期预警的数量激增,以便卫生当局能够迅速作出反应。在本文中,我们提出了一个时间分析工具,用于支持Twitter和官方来源(如世界卫生组织(WHO)和ProMED-mail)之间疾病爆发的比较时间分析。我们自动从官方爆发报告中提取和汇总爆发事件,生成时间序列数据。我们的工具可以支持相关性分析,并基于与官方来源的比较,了解Twitter中提到的爆发的时间发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supporting temporal analytics for health-related events in microblogs
Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.
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