International Relations

Cross-national analysis of global security discourse using word embeddings

Takuto Sakamoto The University of Tokyo
Abstract
This study conducts a systematic investigation of discursive dynamics in the context of policy deliberations on international peace and security, by performing cutting-edge text analysis on the entire body of meeting records of the Security Council of the United Nations for the past quarter of a century (1994-2019). Focusing on one of the most consequential notions for the council’s policy-making, "threat to the peace," it employs an unsupervised machine learning model, broadly termed "word embedding," to analyze how this notion has been discussed by relevant members of the council, especially its five permanent members, during the period under investigation. The study reveals persistent patterns of cross-national convergence and divergence in security discourse, including, most notably, a considerable degree of correlation in how to conceive international threats found between the close-knit Western allies in the council and their Russian counterpart.
Content
Supplementary material
Appendix
This appendix provides further descriptions of the study's methodological aspects. It also reports on additional measurements and analyses that were conducted to examine the robustness of the results discussed in the main text.