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

Stefano Gurciullo*, Slava J. Mikhaylov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationConference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages74-79
Number of pages6
ISBN (Electronic)9781538631485
DOIs
Publication statusPublished - 1 Jul 2017
Event2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017 - Xian, China
Duration: 23 Oct 201725 Oct 2017

Publication series

NameConference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Volume2018-January

Conference

Conference2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Country/TerritoryChina
CityXian
Period23/10/1725/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing
  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications

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