• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International world wide web conference >Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors
【24h】

Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors

机译：地震震动推特用户：社交传感器的实时事件检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a, target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
机译：Twitter是一个流行的微博服务，最近受到了很多关注。 Twitter的一个重要特征是它的实际性质。例如，当发生地震时，人们制造许多与地震相关的Twitter帖子（推文），这使得能够通过观察推文来检测地震发生。如本文所述，我们调查了Twitter中地震等事件的实时交互，并提出了一种监控推文并检测目标事件的算法。要检测目标事件，我们根据推文中的关键字等关键字等特征，设计了推文的分类器，单词和其上下文。随后，我们为目标事件产生概率的时空模型，可以找到活动位置的中心和轨迹。我们将每个Twitter用户视为传感器，并应用卡尔曼滤波和粒子滤波，这些滤波器被广泛用于普遍存在/普遍计算中的位置估计。粒子过滤器比其他可比方法更好，用于估计地震中心和台风的轨迹。作为申请，我们在日本建造了地震报告系统。由于众多地震和全国各地的大量推特用户，我们可以通过监测推文来检测高概率的地震（日本气象局的96％的地震震荡3或更多）。我们的系统及时检测地震，并向注册用户发送电子邮件。通知会比JMA播放的公告更快。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号