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

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

Tracking disaster response and relief following the 2015 Nepal Earthquake

It has been more than one year after the April 2015 Nepal Earthquake. Various support and aid flooded into the affected region, within the entire country and countries around the globe, with extensive media coverage and billion of dollars raised in support of relief/recover efforts. This paper presents analysis of various datasets related to this disaster, including GDELT dataset, showing the rise and fall of people's attention to this event. In addition, financial transaction flows reveal a lot about sources of funds and donations, flow of funds among various organizations or sources, and how these funds and donations have been spent or utilized for relief and recovery efforts. Surveys from citizens in affected areas along with the reconstruction dataset help us to capture the efforts of the international and local organizations and governments made on the post-earthquake relief