Risk analysis and visualization for detecting signs of flood disaster in Twitter

Hoe Yun Kwon, Young Ok Kang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Disasters resulting from climate changes and urbanized complex cities are making it more and more difficult to predict disaster occurrences and their effects. Therefore, detecting signs of disaster can play an important role in minimizing damages and preventing secondary damages through immediate response. Meanwhile, the amount of unstructured data such as SNS data is increasing and efforts to analyze useful information from SNS data are actively being made in various academic areas. In the disaster management area particularly, SNS data can be used as a real-time sensor and premonitory signs showing the possibility of disaster occurrence since the amount of SNS data increases even before the occurrence of disaster. Accordingly, the purpose of this study is to differentiate the risk level of Twitter data and visualize on the map in order to detect signs of disaster as soon as possible. The risk level of Twitter data is a quantified indicator that shows possibility of disaster occurrence and estimated from ‘level of risk expression of tweet text’ and ‘flood vulnerability of tweet location’. As a result, 270,492 tweets that contains signs of flood were extracted and 7090 tweets occurred in Seoul were extracted among 270,492 flooding sign tweets. Second, two indicators of tweet risk level assessment were defined and final risk level rating was assigned based on the risk evaluation matrix. Third, analyzed Twitter data was visualized on time-series map and their applicability was verified based on the flood occurred near Gangnam Station on July 27, 2011. The result shows that tweets about flooding started 2 h prior to when the incident was reported for the first time.

Original languageEnglish
Pages (from-to)127-139
Number of pages13
JournalSpatial Information Research
Volume24
Issue number2
DOIs
StatePublished - 1 Apr 2016

Keywords

  • Disaster risk management
  • Flood disaster
  • Signs of disaster
  • SNS
  • Text analysis
  • Twitter

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