Armed Conflict

Research on armed conflict increasingly leverages advanced computational methods to analyze large datasets, aiming to improve prediction, understanding, and ultimately mitigation of conflict's devastating effects. Current efforts focus on developing machine learning models, including BERT and other deep learning architectures, to analyze textual data from sources like ACLED, predicting conflict fatalities and extracting nuanced information on conflict dynamics from unstructured text. These advancements offer the potential for more accurate conflict forecasting, improved resource allocation for humanitarian aid, and a deeper understanding of the complex social networks and factors driving conflict.