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ISPRS Int. J. Geo-Inf. 2017, 6(7), 204; doi:10.3390/ijgi6070204

Disaster Hashtags in Social Media

1,2,†,‡,* and 3,4,‡
1
Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2
College of Resources and Environment, Graduate University, CAS, Beijing 100049, China
3
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
4
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Current address: 11A, Datun Road, Chaoyang District, Beijing 100101, China.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 30 April 2017 / Revised: 13 June 2017 / Accepted: 30 June 2017 / Published: 5 July 2017
View Full-Text   |   Download PDF [3157 KB, uploaded 5 July 2017]   |  

Abstract

Social media is a rich data source for analyzing the social impact of hazard processes and human behavior in disaster situations; it is used by rescue agencies for coordination and by local governments for the distribution of official information. In this paper, we propose a method for data mining in Twitter to retrieve messages related to an event. We describe an automated process for the collection of hashtags highly related to the event and specific only to it. We compare our method with existing keyword-based methods and prove that hashtags are good markers for the separation of similar, simultaneous incidents; therefore, the retrieved messages have higher relevancy. The method uses disaster databases to find the location of an event and to estimate the impact area. The proposed method can also be adapted to retrieve messages about other types of events with a known location, such as riots, festivals and exhibitions. View Full-Text
Keywords: twitter; hashtags; information retrieval; disasters twitter; hashtags; information retrieval; disasters
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Murzintcev, N.; Cheng, C. Disaster Hashtags in Social Media. ISPRS Int. J. Geo-Inf. 2017, 6, 204.

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