Article
Text mining hurricane Harvey tweet data: Lessons learned and policy recommendations
Text mining hurricane Harvey tweet data: Lessons learned and policy recommendations
(2022)
Abstract
During any crisis, relief efforts depend on the timely exchange of crisis-related information between organizations and the communities. Existing literature shows that relief efforts often fall short in terms of effective communication. One of the possible reasons for this is a misalignment between the expectations of people and the efforts of disaster respondents. Thus, understanding people's needs and expectations during disasters may help reduce the gap between the key stakeholders. In this paper, the authors use the Latent Dirichlet Allocation (LDA) technique to mine tweet data collected during Hurricane Harvey to better understand the needs of the people in the disaster area. Through data mining, the authors identify five themes of concern by Twitter users during the pre-crisis period of Harvey: disaster declaration and emergency response, concern about a specific town, event or travel cancellations, threat to oil & gas (energy) industry, and climate change. Based on these themes, the authors provide recommendations to help disaster management agencies and policymakers be better prepared to assist disaster victims and facilitate citizens' involvement.
Disciplines
Publication Date
February, 2022
Citation Information
Louis Ngamassi, Shahriarirad H, Ram T.R and Rahman Sanusi. "Text mining hurricane Harvey tweet data: Lessons learned and policy recommendations" Text mining hurricane Harvey tweet data: Lessons learned and policy recommendations Vol. 70 (2022) Available at: http://works.bepress.com/louis-ngamassi/2/
Creative Commons license
This work is licensed under a Creative Commons CC_BY-NC International License.