Application of Data Science to Discover Violence-Related Issues in Iraq

TL;DR

This paper demonstrates how data science techniques can be applied to non-governmental big data to identify violence-related social issues in Iraq, achieving notable accuracy despite limited open government data.

Contribution

It introduces a methodology for using global non-governmental data and machine learning algorithms to detect specific violence-related issues in Iraq.

Findings

Decision Trees achieved the best accuracy of 0.7629 for refugee crises and artillery fights.

The approach successfully identified locations with high precision and recall.

The methodology addresses data scarcity challenges in conflict zones.

Abstract

Data science has been satisfactorily used to discover social issues in several parts of the world. However, there is a lack of governmental open data to discover those issues in countries such as Iraq. This situation arises the following questions: how to apply data science principles to discover social issues despite the lack of open data in Iraq? How to use the available data to make predictions in places without data? Our contribution is the application of data science to open non-governmental big data from the Global Database of Events, Language, and Tone (GDELT) to discover particular violence-related social issues in Iraq. Specifically we applied the K-Nearest Neighbors, N\"aive Bayes, Decision Trees, and Logistic Regression classification algorithms to discover the following issues: refugees, humanitarian aid, violent protests, fights with artillery and tanks, and mass killings. The best results were obtained with the Decision Trees algorithm to discover areas with refugee crises and artillery fights. The accuracy for these two events is 0.7629. The precision to discover the locations of refugee crises is 0.76, the recall is 0.76, and the F1-score is 0.76. Also, our approach discovers the locations of artillery fights with a precision of 0.74, a recall of 0.75, and a F1-score of 0.75.