Title: Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings,
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.