Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

ISPRS International Journal of Geo-Information

Qunying Huang¹  Yu Xiao³  Christoph Aubrecht² 

[1] Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA;id="af1-ijgi-04-01549">1Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, U;Department of Landscape Architecture & Urban Planning, Hazard Reduction and Recovery Center, Texas A&M University, College Station, TX 77843, USA; E-Mail:

关键词: social media;    disaster;    text mining;    data mining;    disaster coordination;    disaster relief;   

DOI  :  10.3390/ijgi4031549

来源: mdpi

【 摘 要 】

Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.