Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes

ABSTRACT Scholars have been interested in the politicization of humans rights within the United Nations for some time. However, previous research typically looks at simple associations between voting coalitions and observable variables, such as geographic location or membership in international organizations. Our study is the first attempt at estimating the latent coalition structure based on the voting data. We propose a Bayesian Dynamic Dirichlet Process Mixture (DDPM) model to identify voting coalitions based on roll call vote data across multiple time periods. We also propose post-processing methods for analyzing the outputs of the DDPM model. We apply these methods to the United Nations General Assembly (UNGA) human rights roll call vote data from 1992 to 2017. We identify human rights voting coalitions in the UNGA after the Cold War, and polarizing resolutions that divide countries into different coalitions.

• Publication year

  2021

• Venue

  Journal of Applied Statistics

• Publication date

  2021-05-20

• Fields of study

  Medicine, Computer Science, Political Science

• Identifiers
  DOI 10.1080/02664763.2021.1931820PMID 36035611PMCID PMC9415553
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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