# Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis

arXiv: https://arxiv.org/abs/2302.02267 · HTML: https://mlsanity.com/paper/2302.02267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [Identifying Actionable Messages on Social Media](https://mlsanity.com/paper/1511.00722/) (2015)
- [A Review of Situation Awareness Assessment Approaches in Aviation Environments](https://mlsanity.com/paper/1803.08067/) (2018)
- [Deep Learning and Word Embeddings for Tweet Classification for Crisis Response](https://mlsanity.com/paper/1903.11024/) (2019)

## Abstract (arXiv)

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.