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A Machine Learning Approach for Detecting Rescue Requests from Social Media

Authors :
Zheye Wang
Nina S. N. Lam
Mingxuan Sun
Xiao Huang
Jin Shang
Lei Zou
Yue Wu
Volodymyr V. Mihunov
Source :
ISPRS International Journal of Geo-Information, Vol 11, Iss 11, p 570 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently.

Details

Language :
English
ISSN :
11110570 and 22209964
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.9bcbc01cb6324293b84fa43ddb01f843
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi11110570