In this research, we focused on armed conflicts and related violence. The study reviewed the use of machine learning to predict the likelihood of conflict escalation and the role of conditioning factors. The results showed that machine learning and predictive models could help identify conflict-prone locations and geospatial factors contributing to conflict escalation. The study found 46 relevant papers and emphasized the importance of considering unique predictors and conditioning factors for each conflict. It was found that the conflict susceptibility of a region is influenced principally by its socioeconomic conditions and its political/governance factors. We concluded that machine learning has the potential to be a valuable tool in conflict analysis and, therefore, it can be an asset in conflict mitigation and prevention, but the accuracy of the models depends on data quality and the careful selection of conditioning factors. Future research should aim to refine the methodology for more accurate prediction of the models.
Abstract. Globally, the absolute number of war deaths has been declining since 1946. And yet, conflict and violence are currently on the rise, with many conflicts today waged between non-state actors such as political militias, criminal, and international terrorist groups. Unresolved regional tensions, a breakdown in the rule of law, absent or co-opted state institutions, illicit economic gain, and the scarcity of resources exacerbated by climate change, have become dominant drivers of conflict (UN. A new era of conflicts, 2022).In the ear of modern technology, data science, machine learning, and AI, the available shall be used to analyze, understand and possibly predict the possibility of conflicts outbreaks in various parts of the world. Moreover, it should provide tools for political scientists to a deeper understanding of political processes and enhance their decision-making processes.This paper focuses on applying data science techniques to process and analyze data in three various data analysis domains: Semantic, Geospatial, and Temporal Analysis. It provides the possible sources of the conflict and other datasets used for the analytics mentioned above. The data is used for research and experimental purposes only. These analytical processes provide the mechanisms to discover the historical data and identify the potential causes of the conflicts.
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