Intelligent Disaster Response via Social Media Analysis A Survey <s> Warning Systems <s> The United States has no comprehensive national warning strategy that covers all hazards in all places. Instead, public warning practices are decentralized across different governments and the private sector. Uneven preparedness to issue warnings exists across local communities; hence, people are unevenly protected from the surprise onset of natural disasters. Without changes in this situation, inequalities will grow larger, and the gains made in saving lives over the past decades may well be reversed. Since the first assessment of research on natural hazards was completed in 1975, there have been significant improvements in forecasts and warnings for some hazards but only marginal improvements for others. Forecasts for floods, hurricanes, and volcanic eruptions have improved most significantly, and public dissemination of warnings has improved the most for hurricanes. However, a 100% reliable warning system does not exist for any hazard. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Warning Systems <s> 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. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Warning Systems <s> Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems. <s> BIB003
"Warning systems detect impending disaster, give that information to people at risk, and enable those in danger to make decisions and take action" BIB001 . There has been a significant improvement in forecasting and warning systems especially for hurricane and earthquakes. Meteorologists can now forecast a hurricane 2 to 6 days before it hits an area and Global Seismic Network constantly monitors activity bellow Earth's surface. However, lack of complete data on natural hazards, monitoring instruments, and high dynamic nature of them keep accurate forecasting and warning a challenge  and "a 100% reliable warning system does not exist for any hazard BIB001 ". Social media facilitates is also used to deliver official and non-official warnings. Emergency managers and governmental organization post their warning messages via social media to be broadly accessed by the public [36] . Citizens also report warnings and advice about possible hazards BIB003 . One source of information that can be used to improve accuracy of warnings is data of built-in accelerometers in cell phones. This data can be used for quick detection of earthquakes and estimating their intensity and effect. The measurements by these sensors which are transmitted before the loss of communication are used for estimating the degree of damage to different areas; the task that can take up to an hour when performed by helicopters [27; 28] . Social media is another source of information for warning systems. USGS uses tweets to check the accuracy of sensor reports and faster detection of earthquakes. Disasters such as Sichuan earthquake in 2008 show that Twitter is faster at reporting earthquakes than USGS. Earthquakes can be detected using tweets by 60 seconds earlier than sensors which is a valuable time for warning areas under danger and start evacuation  . In another effort, Sakaki et al. BIB002 consider each Twitter user as a sensor. The tweets by these sensors will be used to detect the occurrence of disasters and estimate their location.