The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

Christopher Loynes (), Jamal Ouenniche () and Johannes Smedt ()
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Jamal Ouenniche: University of Edinburgh Business School Management Science and Business Economics Group
Johannes Smedt: University of Edinburgh Business School Management Science and Business Economics Group

Annals of Operations Research, 2022, vol. 308, issue 1, No 13, 339-371

Abstract: Abstract This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster’s geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further.

Keywords: Disaster detection; Disaster management; Location estimation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-020-03684-8

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