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Predicting Partisan Responsiveness: A Probabilistic Text Mining Time-Series Approach

Published online by Cambridge University Press:  21 June 2019

Lenka Bustikova Open the ORCID record for Lenka Bustikova [Opens in a new window] ,
David S. Siroky Open the ORCID record for David S. Siroky [Opens in a new window] ,
Saud Alashri Open the ORCID record for Saud Alashri [Opens in a new window]  and
Sultan Alzahrani Open the ORCID record for Sultan Alzahrani [Opens in a new window]
Lenka Bustikova*
Affiliation:
Associate Professor, School of Politics and Global Studies, Arizona State University, Tempe, AZ, USA. Email: [email protected]
David S. Siroky
Affiliation:
Associate Professor, School of Politics and Global Studies, Arizona State University, Tempe, AZ, USA. Email: [email protected]
Saud Alashri*
Affiliation:
Assistant Professor, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia. Email: [email protected], [email protected]
Sultan Alzahrani*
Affiliation:
Assistant Professor, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia. Email: [email protected], [email protected]
*
*Email: [email protected], [email protected], [email protected]
*Email: [email protected], [email protected], [email protected]
*Email: [email protected], [email protected], [email protected]
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Abstract

When do parties respond to their political rivals and when do they ignore them? This article presents a new computational framework to detect, analyze and predict partisan responsiveness by showing when parties on opposite poles of the political spectrum react to each other’s agendas and thereby contribute to polarization. Once spikes in responsiveness are detected and categorized using latent Dirichlet allocation, we utilize the terms that comprise the topics, together with a gradient descent solver, to assess the classifier’s predictive accuracy. Using 10,597 documents from the official websites of radical right and ethnic political parties in Slovakia (2004–2014), the analysis predicts which political issues will elicit partisan reactions, and which will be ignored, with an accuracy of 83% (F-measure) and outperforms both Random Forest and Naive Bayes classifiers. Subject matter experts validate the approach and interpret the results.

Keywords

forecastingautomated content analysisnatural language processingprobabilistic topic modelingsparse learningpolitical partiespolarization
Type
Articles
Information
Political Analysis , Volume 28 , Issue 1 , January 2020 , pp. 47 - 64
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Author’s note: We thank Ben Ansell, David Art, Kai Arzheimer, Daniel Berliner, Anita Bodlos, Rebecca Cordell, Stefan Dahlberg, Hasan Davulcu, Pieter Dewilde, Valery Dzutsati, Michael Hechter, Sean Kates, Miki Kittilson, Will Moore, Andrea Pirro, Mark Ramirez, Christian Rauh, Seyedbabak Rezaeedaryakenari, Martijn Schoonvelde, Gijs Schumacher, Sarah Shair-Rosenfield, Arthur Spirling, Scott Swagerty, Cameron Thies, Joshua Tucker, Carolyn Warner, Reed Wood, Thorin Wright and two anonymous reviewers for comments. Earlier versions of the paper were presented in Amsterdam at the EU-Engage Automated Text Analysis Conference, hosted by Gijs Schumacher and Martijn Schoonvelde, at the American Political Science Association Conference in 2015 and at the School of Politics and Global Studies Workshop. The project received seed funding from the Center for the Study of Religion and Conflict at ASU. We especially thank Carolyn Forbes for helping to initiate and sustain the project. Supplementary materials for this article are available on the Political Analysis website. For Dataverse replication materials, see Alashri et al. (2018).

Contributing Editor: R. Michael Alvarez

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