Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings
This work addresses a problem in foreign policy analysis for researchers and policymakers, offering incremental improvements through new computational methods.
The paper tackled the challenge of measuring policy preferences and shifts in international politics by applying a neural word embedding model to UN General Debate speeches, resulting in policy attention indices, country-specific semantic centrality indices, and a falsified hypothesis linking speech content to UN voting behavior.
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.