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.

翻译：本研究探讨了危机管理中使用的机器学习方法。分析危机检测模式涉及通过自动化手段收集和评估历史或近实时数据集。本文采用元综述方法，分析了利用机器学习技术评估危机中人类行为的科学文献。通过对三个学术数据库中已发表文献进行系统评估，将选定的研究归纳为主题和新兴趋势。结果显示，社交媒体数据在被评估文章中最突出，使用率达27%，其次是灾害管理、健康（COVID）和危机信息学等众多主题。此外，监督式机器学习方法在各领域应用广泛，使用率达69%，占据主导地位。分类任务在机器学习任务中脱颖而出，使用率为41%。发挥主要作用的算法包括支持向量机、神经网络、朴素贝叶斯和随机森林，其贡献率分别为23%、16%、15%和12%。