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The UN Security Council Transcripts: from Automated Topic Modeling to Speeches-As-Network

Foreign Policy
Security
UN
Big Data
Mirco Schoenfeld
Technical University of Munich
Mirco Schoenfeld
Technical University of Munich
Steffen Eckhard
Universität Konstanz
Alena Moiseeva
Ludwig-Maximilians-Universität München – LMU
Ronny Patz
Ludwig-Maximilians-Universität München – LMU
Hilde van Meegdenburg
Departments of Political Science and Public Administration, Universiteit Leiden

Abstract

The United Nations Security Council (UNSC) is the highest authority in the domain of international security. It brings together the global powers China, France, Russia, the United Kingdom and the United States as Permanent Five members and changing constellations of ten other, non-permanent members. Most meetings are registered, transcribed, and translated to all official UN languages in public protocols. Each meeting covers one major topic of geopolitical importance, often related to single countries or conflicts. We propose a novel method of analyzing political topic networks based on these UNSC protocols. Distilling a corpus of speeches together with information about each speaker's nationality from meeting protocols through automated speech extraction, we model the topics discussed by the speaker with LDA-based topic modeling techniques. Based on these country-topic dyads, we construct two-mode political topic networks. From meeting to meeting, different topical closeness between participants becomes apparent, and the resulting network portrays international diplomatic relations between participants of UNSC meetings. For this paper, and selecting from the huge online database of UNSC protocols and speeches, we focus on all meetings regarding Afghanistan in the period from 2001 until 2017. This confined subset allows to qualitatively validate the automated extraction, topic-modeling and speeches-as-network display: The timeframe of external events as well as actors’ preferences are known from previous studies and can be juxtaposed with the results of the automated analysis. The paper demonstrates first that the automated content analysis is able to identify sub-topics in the Afghanistan debate as well as the trends in their relative importance over time. Second, the network analysis confirms that in this debate Permanent Five and non-permanent members emphasize different sub-topics. These findings underline how automated topic modeling is able to represent key structures of differentiation in the international system alongside overall sub-topics trends over time. We discuss these findings as well as open methodological challenges in view of scaling up our analysis to a complete corpus of UN Security Council speeches.