Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis
It provides a systematic review of existing methods for evaluating public crises, which is incremental as it synthesizes prior research without introducing new techniques.
This study conducted a meta-analysis of machine learning methods used in crisis management, finding that supervised learning (69% usage) and classification techniques (41% usage) were predominant, with social media data being the most common source (27% usage).
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.