Citation

Kankanamge N, Yigitcanlar T, Goonetilleke A, Kamruzzaman M. Int. J. Disaster Risk Reduct. 2020; 42: e101360.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2019.101360

PMID

unavailable

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.

Language: en

Keywords

Big data; Crowdsourcing; Data analytics; Social media; South East Queensland Floods; Volunteered geographic information