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What drives the international development agenda? An NLP analysis of the United Nations General Debate 1970-2016

Baturo, Alexander orcid logoORCID: 0000-0002-1108-5287 and Dasandi, Niheer orcid logoORCID: 0000-0002-8708-837X (2018) What drives the international development agenda? An NLP analysis of the United Nations General Debate 1970-2016. In: International Conference on the Frontiers and Advances in Data Science (FADS), 23-25 Oct. 2017, Xi'an, China.

Abstract
Surprisingly little is known about agenda setting for international development in the United Nations (UN), despite it having a significant influence on the process and outcomes of development efforts. This paper addresses this shortcoming using a novel approach that applies natural language processing techniques to countries’ annual statements in the UN General Debate. Every year UN member states deliver statements during the General Debate on their governments’ perspective on major issues in world politics. These speeches provide invaluable information on state preferences on a wide range of issues, including international development, but have largely been overlooked in the study of global politics. This paper identifies the main international development topics that states raise in these speeches between 1970 and 2016, and examine the country-specific drivers of international development rhetoric.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Government; Coherence; Semantics; Economics; Security; Speech; Biological system modeling
Subjects:Social Sciences > International relations
Social Sciences > Political science
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Law and Government
Published in: International Conference on the Frontiers and Advances in Data Science (FADS),Proceedings of. . IEEE.
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/FADS.2017.8253221
Copyright Information:© 2017 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:25595
Deposited On:08 Mar 2021 16:02 by Thomas Murtagh . Last Modified 08 Mar 2021 16:02
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