Alvan Arulandu

Brian Zhou

We aggregate corpora from UN General Debates and emerging media for conflict prediction.

A Framework to Apply Natural Language Processing Techniques to Analyze Public Opinions on Peace and Governance in Africa

We analyze the UN General Debate Corpus to track the influence of African post-colonial states, using natural language processing to forecast shifts in global priorities and the efficacy of crisis resolution measures.

The effectiveness of international bodies such as the United Nations (UN) at addressing global crises has been debated. The updated UN General Debate Corpus (UNGDC) catalogues every speech from the UN's inception in 1946 to 2022. Using the corpus as an indicator of debate, we explore how African post-colonial states grow influence on the international stage. As these states join the UN immediately after independence, we superimpose historical events on metrics generated from UNGDC to demonstrate the corpus' robustness for forecasting shifts in international priorities. We develop a time series of relevance for each country using natural language processing methods to extract features and tokenize speeches. We conclude that the UNGD preludes intervention in multi-year violent conflicts, with insights into the efficacy of crisis resolution measures in Africa. Our results are established by computational experiments, with conditions validated by statistical significance tests.

Papers coauthored with Alvan Arulandu

Publications

A Framework to Apply Natural Language Processing Techniques to Analyze Public Opinions on Peace and Governance in Africa

Analyzing the Discourse in the UN for Crisis Response in Post-Colonial Africa