brian/
Publications

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

Alvan Arulandu, Brian Zhou
IEEE MIT URTC, 2023
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BibTeX
@INPROCEEDINGS{10534975,
  author={Arulandu, Alvan Caleb and Zhou, Brian},
  booktitle={2023 IEEE MIT Undergraduate Research Technology Conference (URTC)}, 
  title={Analyzing the Discourse in the UN for Crisis Response in Post-Colonial Africa}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Measurement;Time series analysis;Africa;Feature extraction;Robustness;Natural language processing;Speech processing;United Nations;UN General Assembly General Debate;UN General Debate Corpus;political communication;text as data;natural language processing},
  doi={10.1109/URTC60662.2023.10534975}}
Analyzing the Discourse in the UN for Crisis Response in Post-Colonial Africa

Abstract

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.

See also