Application of Data Science to Discover Violence-Related Issues in Iraq
- URL: http://arxiv.org/abs/2006.07980v1
- Date: Sun, 14 Jun 2020 18:58:25 GMT
- Title: Application of Data Science to Discover Violence-Related Issues in Iraq
- Authors: Merari Gonz\'alez, Germ\'an H. Alf\'erez
- Abstract summary: There is a lack of governmental open data to discover social issues in Iraq.
Our contribution is the application of data science to open non-governmental big data to discover violence-related social issues in Iraq.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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