Social Media Information Sharing for Natural Disaster Response
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by
Zhijie Sasha Dong, Lingyu Meng, Lauren Christenson, Lawrence Fulton
2021
Abstract
Social media has become an essential channel for posting disaster-related
information, which provide governments and relief agencies real-time data for
better disaster management. However, research in this field has not received
sufficient attention and extracting useful information is still challenging.
This paper aims to improve disaster relief efficiency via mining and analyzing
social media data like public attitudes towards disaster response and public
demands for targeted relief supplies during different types of disasters. We
focus on different natural disasters based on properties such as types,
durations, and damages, which contains a total of 41,993 tweets. In this paper,
public perception is assessed qualitatively by manually classified tweets,
which contain information like the demand for targeted relief supplies,
satisfactions of disaster response, and public fear. Public attitudes to
natural disasters are studied via a quantitative analysis using eight machine
learning models. To better provide decision-makers with the appropriate model,
the comparison of machine learning models based on computational time and
prediction accuracy is conducted. The change of public opinion during different
natural disasters and the evolution of people's behavior of using social media
for disaster relief in the face of the identical type of natural disasters as
Twitter continues to evolve are studied. The results in this paper demonstrate
the feasibility and validation of the proposed research approach and provide
relief agencies with insights into better disaster management.
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