Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis
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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 (COVID) and crisis informatics, amongst many other themes. Additionally, the supervised machine learning method, with an application of 69 board, was predominant. The classification technique stood out among other machine learning tasks with 41 were the Support Vector Machine, Neural Networks, Naive Bayes, and Random Forest, with 23
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