Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings

TL;DR

This paper applies neural word embeddings to analyze UN General Debate speeches, creating indices to measure policy focus and semantic relationships, and explores their relation to voting behavior, offering new tools for foreign policy analysis.

Contribution

It introduces novel semantic indices derived from word embeddings and tests their relation to voting, providing new quantitative tools for policy preference detection.

Findings

Semantic proximity indices reflect policy attention.

Country semantic centrality measures relationships.

Speech content does not directly predict voting outcomes.

Abstract

Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads of state or government in the United Nations General Debate. The paper provides three key contributions based on the output of the Word2vec model. First, it presents a set of policy attention indices, synthesizing the semantic proximity of political speeches to specific policy themes. Second, it introduces country-specific semantic centrality indices, based on topological analyses of countries' semantic positions with respect to each other. Third, it tests the hypothesis that there exists a statistical relation between the semantic content of political speeches and UN voting behavior, falsifying it and suggesting that political speeches contain information of different nature then the one behind voting outcomes. The paper concludes with a discussion of the practical use of its results and consequences for foreign policy analysis, public accountability, and transparency.