Extracting Geospatial Information from Social Media Data for Hazard Mitigation, Typhoon Hato as Case Study (Short Paper)

Authors Jibo Xie, Tengfei Yang, Guoqing Li

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Author Details

Jibo Xie
  • Institute of Remote Sensing and Digital Earth, No.9 Dengzhuang South Rd, Haidian District, Beijing 100094, China
Tengfei Yang
  • Institute of Remote Sensing and Digital Earth
  • University of Chinese Academy of Sciences, No.9 Dengzhuang South Rd, Haidian District,Beijing 100094
  • Beijing 100049, China
Guoqing Li
  • Institute of Remote Sensing and Digital Earth, No.9 Dengzhuang South Rd, Haidian District, Beijing 100094, China

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Jibo Xie, Tengfei Yang, and Guoqing Li. Extracting Geospatial Information from Social Media Data for Hazard Mitigation, Typhoon Hato as Case Study (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 65:1-65:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


With social media widely used for interpersonal communication, it has served as one important channel for information creation and propagation especially during hazard events. Users of social media in hazard-affected area can capture and upload hazard information more timely by portable and internet-connected electric devices such as smart phones or tablet computers equipped with (Global Positioning System) GPS devices and cameras. The information from social media(e.g. Twitter, facebook, sina-weibo, WebChat, etc.) contains a lot of hazard related information including texts, pictures, and videos. Most important thing is that a fair proportion of these crowd-sourcing information is valuable for the geospatial analysis in Geographic information system (GIS) during the hazard mitigation process. The geospatial information (position of observer, hazard-affected region, status of damages, etc) can be acquired and extracted from social media data. And hazard related information could also be used as the GIS attributes. But social media data obtained from crowd-sourcing is quite complex and fragmented on format or semantics. In this paper, we introduced the method how to acquire and extract fine-grained hazard damage geospatial information. According to the need of hazard relief, we classified the extracted information into eleven hazard loss categories and we also analyzed the public's sentiment to the hazard. The 2017 typhoon "Hato" was selected as the case study to test the method introduced.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Social media
  • Information systems → Geographic information systems
  • Computing methodologies → Information extraction
  • Human-centered computing → Geographic visualization
  • Social media
  • hazard mitigation
  • GIS
  • information extraction
  • typhoon


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