Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis

TL;DR

This meta-analysis reviews machine learning applications in crisis management, highlighting social media data and supervised classification techniques, especially SVMs and neural networks, as key tools in evaluating human actions during crises.

Contribution

It provides a systematic review of existing literature, identifying dominant data sources, methods, and algorithms used in machine learning for crisis evaluation.

Findings

Social media data is the most used source at 27%.

Supervised machine learning methods account for 69% of applications.

Support Vector Machines and Neural Networks are the most common algorithms.

Abstract

This study examines machine learning methods used in crisis management. Analyzing detected patterns from a crisis involves the collection and evaluation of historical or near-real-time datasets through automated means. This paper utilized the meta-review method to analyze scientific literature that utilized machine learning techniques to evaluate human actions during crises. Selected studies were condensed into themes and emerging trends using a systematic literature evaluation of published works accessed from three scholarly databases. Results show that data from social media was prominent in the evaluated articles with 27% usage, followed by disaster management, health (COVID) and crisis informatics, amongst many other themes. Additionally, the supervised machine learning method, with an application of 69% across the board, was predominant. The classification technique stood out among other machine learning tasks with 41% usage. The algorithms that played major roles were the Support Vector Machine, Neural Networks, Naive Bayes, and Random Forest, with 23%, 16%, 15%, and 12% contributions, respectively.