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Detecting policy preferences and dynamics in the un general debate with neural word embeddings

Gurciullo, S and Mikhaylov, SJ (2018) Detecting policy preferences and dynamics in the un general debate with neural word embeddings. In: 2017 International Conference on the Frontiers and Advances in Data Science (FADS), 2017-10-23 - 2017-10-25, Xi'an, China.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Subjects: J Political Science > JA Political science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Social Sciences > Government, Department of
Depositing User: Elements
Date Deposited: 05 Sep 2018 12:50
Last Modified: 05 Sep 2018 12:50
URI: http://repository.essex.ac.uk/id/eprint/22940

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