Predicting and detecting political crisis in the conflict-intense area of Middle East is of important to political leaders so that some tragedy might be prevented in advance to occur if predicted. Based on monthly political event data we offer an enhanced approach to build machine learning models in order to predict the future intensity of major conflict specifically in Middle East. By analyzing the inner connections and relationships of entities of political events in Middle East, it is revealed that these additional features can to a considerably large extent improve predictive performance when included in models. We investigate political event data of Middle East area across almost ten years โ from Jan. 2005 to May. 2014. So far, graph-based analysis is combined with four machine models in our research to make predictions, and is evaluated by ground truth extracted from GTDS, a categorized crisis dataset. The results indicate that by introducing features from graph, the overall accuracy of prediction has been improved by 11.4%, from 70.3% to 81.7%. Graph-based analysis in dealing with problem regarding political events and crisis is hereby proved to be exceeding effective, and should be considered as an outstanding alternative in future studies.
crisis-detection-and-prediction's Introduction
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