A Discourse-Driven Intervention Recommendation Framework for United Nations Peacekeeping in Post-Colonial Africa

The United Nations (UN) is the foremost international body helping uphold world peace through peacekeeping missions, ranging from deployments that enforce peace treaties, monitor conflicts, and protect civilians; However, determining when and how to intervene is complex. The updated UN General Debate Corpus (UNGDC), cataloging every speech from the UN's inception in 1946 to 2022, is a treasure trove of national policy, as the UNGD is the only body where every country can speak. We propose a discourse-driven intervention recommendation framework that categorizes ongoing conflicts based on UN precedent and recommends the magnitude of funds and forces that should be committed to addressing a conflict. We employ natural language processing techniques to tokenize, preprocess, and analyze word stem frequencies in the UNGDC, generating a timeseries of the number of UN mentions for any given country. Paired with historical analysis, we show that debate in the UNGDC is a potent indicator to determine UN intervention and response mechanisms for conflicts in Africa; further, by aggregating mention statistics across periods of active conflict, we provide quantitative backing for the correlation of mention dynamics and the presence of an active conflict, for a given country. Finally, we present and test an interpretable, shallow decision tree model that can perform intervention type classification and response magnitude recommendation with 91.7% accuracy. Our results, established by computational experiments and statistical testing, suggest that corpus analysis and broader computational diplomacy methods can drive intervention recommendations to improve the UN’s decision-making.