Determining disaster severity through social media analysis: Testing the methodology with South East Queensland Flood tweets

Nayomi Kankanamge, Tan Yigitcanlar, Ashantha Goonetilleke, Md Kamruzzaman

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.
Original languageEnglish
Article number101360
Number of pages13
JournalInternational Journal of Disaster Risk Reduction
Volume42
DOIs
Publication statusPublished - Jan 2020

Keywords

  • social media
  • data analytics
  • big data
  • crowdsourcing
  • volunteered geographic information
  • South East Queensland floods

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