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Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach

Published online by Cambridge University Press:  13 March 2017

Derek Greene
Affiliation:
Insight Centre for Data Analytics & School of Computer Science, University College Dublin, Ireland. Email: derek.greene@ucd.ie
James P. Cross*
Affiliation:
School of Politics & International Relations, University College Dublin, Ireland. Email: james.cross@ucd.ie

Abstract

This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999 to 2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro Crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behavior and the committee structure of the Parliament.

Type
Articles
Copyright
Copyright © The Author(s) 2017. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: The authors would like to thank Prof. David Farrell, Dr. Jos Elkink, and the panel participants at the 2015 EPSA General Conference, the 2015 ACM Web Science Conference, and workshops in UCD, Dublin and the EUENGAGE Automated Text Analysis Workshop in Amsterdam for their helpful comments. We would also like to thank the editor and the three anonymous reviewers who provided constructive feedback that significantly improved the final paper. For Dataverse replication materials, see Cross and Greene (2016).This research was partly supported by the Irish Research Council (IRC) New Foundations Scheme and the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

Contributing Editor: R. Michael Alvarez

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