Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-23T07:41:46.143Z Has data issue: false hasContentIssue false
  • English
  • Français

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: lenka.bustikova@asu.edu
David S. Siroky
Affiliation:
Associate Professor, School of Politics and Global Studies, Arizona State University, Tempe, AZ, USA. Email: david.siroky@asu.edu
Saud Alashri*
Affiliation:
Assistant Professor, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia. Email: salashri@kacst.edu.sa, szahrani@kacst.edu.sa
Sultan Alzahrani*
Affiliation:
Assistant Professor, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia. Email: salashri@kacst.edu.sa, szahrani@kacst.edu.sa
*
*Email: lenka.bustikova@asu.edu, salashri@kacst.edu.sa, szahrani@kacst.edu.sa
*Email: lenka.bustikova@asu.edu, salashri@kacst.edu.sa, szahrani@kacst.edu.sa
*Email: lenka.bustikova@asu.edu, salashri@kacst.edu.sa, szahrani@kacst.edu.sa
Get access Rights & Permissions [Opens in a new window]

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. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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

References

Abelson, R. P. 1973. “The Structure of Belief Systems.” In Computer Models of Thought and Language , edited by Schank, R. and Colby, K. K. M., 287–339. San Francisco: W. H. Freeman and Company.Google Scholar
Abou-Chadi, T., and Krause, W.. 2018. “The Causal Effect of Radical Right Success on Mainstream Parties’ Policy Positions: A Regression Discontinuity Approach.” British Journal of Political Science , doi:10.1017/S0007123418000029.Google Scholar
Adams, J. 2012. “Causes and Electoral Consequences of Party Policy Shifts in Multiparty Elections: Theoretical Results and Empirical Evidence.” The Annual Review of Political Science 15(1):401–419.Google Scholar
Adams, J., Clark, M., Ezrow, L., and Glasgow, G.. 2006. “Are Niche Parties fundamentally Different from Mainstream Parties? The Causes and the Electoral Consequences of Western European Parties’ Policy Shifts.” American Journal of Political Science 50(3):513–529.Google Scholar
Adams, J., and Somer-Topcu, Z.. 2009. “Policy Adjustment by Parties in Response to Rival Parties’ Policy Shifts: Spatial Theory and the Dynamics of Party Competition in Twenty-Five Post-War Democracies.” British Journal of Political Science 39(4):825–846.Google Scholar
Alashri, S., Alzahrani, S., Bustikova, L., and Siroky, D.. 2018. “Replication Data for: Predicting Partisan Responsiveness: A Probabilistic Text Mining Time-Series Approach.” https://doi.org/10.7910/DVN/TBW4Y4, Harvard Dataverse, V1.Google Scholar
Alashri, S., Alzahrani, S., Bustikova, L., Siroky, D., and Davulcu, H.. 2015. “What Animates Political Debates? Analyzing Ideological Perspectives in Online Debates Between Opposing Parties.” In Proceedings of the ASE/IEEE International Conference on Social Computing (SocialCom-15) . Stanford, CA: Academy of Science and Engineering.Google Scholar
Arceneaux, K., and Johnson, M.. 2015. “More a Symptom Than a Cause: Polarization and Partisan News Media in America.” In American Gridlock: The Sources, Character, and Impact of Political Polarization , edited by Thurber, J. A. and Yoshinaka, A., 309–336. New York: Cambridge University Press.Google Scholar
Arzheimer, K., and Carter, E.. 2009. “Christian Religiosity and Voting for West European Radical Right Parties.” West European Politics 32(5):985–1011.Google Scholar
Baboš, P., Világi, A., and Oravcová, V.. 2016. Spoločenské problémy a politické (ne)riešenia: Vol’by 2016 , Bratislava: STIMUL.Google Scholar
Blei, D. M., Griffiths, T. L., and Jordan, M. I.. 2010. “The Nested Chinese Restaurant Process and Bayesian Nonparametric Inference of Topic Hierarchies.” Journal of the ACM 57(2):1–30.Google Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I.. 2002. “Latent Dirichlet Allocation.” Advances in Neural Information Processing Systems (NIPS) 14 3:601–608.Google Scholar
Boyce, B. R. 1990. “Concepts of Information Retrieval and Automatic Text Processing: The Transformation Analysis, and Retrieval of Information by Computer.” Journal of the American Society for Information Science 41(2):150–151.Google Scholar
Breiman, L. 2001. “Random Forests.” Mach. Learn. 45(1):5–32.Google Scholar
Bustikova, L. 2014. “Revenge of the Radical Right.” Comparative Political Studies 47(12):1738–1765.Google Scholar
Bustikova, L., and Kitschelt, H.. 2009. “The Radical Right in Post-Communist Europe. Comparative Perspectives on Legacies and Party Competition.” Communist and Post-Communist Studies 42(4):459–483.Google Scholar
Bútora, M. 2007. “Nightmares from the Past, Dreams of the Future.” Journal of Democracy 18(4):47–55.Google Scholar
Carbonell, J. G. 1978. “POLITICS: Automated Ideological Reasoning.” Cognitive Science 2(1):27–51.Google Scholar
Colaresi, M., and Mahmood, Z.. 2017. “Do the Robot: Lessons from Machine Learning to Improve Conflict Forecasting.” Journal of Peace Research 54(2):193–214.Google Scholar
Coughlin, R. M., and Lockhart, C.. 1998. “Grid-Group Theory and Political Ideology: A Consideration of Their Relative Strengths and Weaknesses for Explaining the Structure of Mass Belief Systems.” Journal of Theoretical Politics 10(1):33–58.Google Scholar
Deegan Krause, K, and Haughton, T.. 2009. “Toward a More Useful Conceptualization of Populism.” Politics and Policy 37(4):821–841.Google Scholar
Denny, M., and Spirling, A.. 2018. “Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It.” Political Analysis 26(2):168–189.Google Scholar
Douglas, M. 1970. Natural Symbols. Explorations in Cosmology . London: Routledge.Google Scholar
Douglas, M., and Wildavsky, A.. 1982. Risk and Culture: An Essay on the Selection of Technical and Environmental Dangers , Berkeley, CA: University of California Press.Google Scholar
Eggers, A. C., and Spirling, A.. 2018. “The Shadow Cabinet in Westminster Systems: Modeling Opposition Agenda Setting in the House of Commons.” British Journal of Political Science 48(2):343–367.Google Scholar
Evans, J. A. 2002. “In Defence of Sartori: Party System Change, Voter Preference Distributions and Other Competitive Incentives.” Party Politics 8(2):155–174.Google Scholar
Greene, D., and Cross, J. P.. 2017. “Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach.” Political Analysis 25(1):77–94.Google Scholar
Grendstad, G. 2003. “Comparing Political Orientations: Grid-group Theory Versus the Left-Right Dimension in the Five Nordic Countries.” European Journal of Political Research 42(1):1–21.Google Scholar
Grimmer, J. 2009. “A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in State Press Releases.” Political Analysis 18(1):1–51.Google Scholar
Grimmer, J., and Stewart, B. M.. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3):267–297.Google Scholar
Guasti, P., and Mansfeldová, Z.. 2018. Democracy Under Stress . Prague, Czech Republic: Institute of Sociology, Czech Academy of Sciences.Google Scholar
Gyárfášová, O. 2015. “To Sladké Slovo Demokracia…. Spokojnost’ s Demokraciou a Politické Odcudzenie na Slovensku.” Sociológia–Slovak Sociological Review 47(4):365–389.Google Scholar
Hartigan, J. A., and Wong, M. A.. 1979. “Algorithm AS 136: A K-Means Clustering Algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1):100–108. doi:10.2307/2346830.Google Scholar
Haughton, T., and Ryba, M.. 2008. “A Change in Direction: the 2006 Parliamentary Elections and Party Politics in Slovakia.” Journal of Communist Studies and Transition Politics 24:232–255.Google Scholar
Ignazi, P. 1992. “The Silent Counter-Revolution.” European Journal of Political Research 22(1):3–34.Google Scholar
Katz, R. S., and Mair, P.. 1995. “Changing Models of Party Organization and Party Democracy: The Emergence of the Cartel Party.” Party Politics 1(1):5–28.Google Scholar
Kluknavská, A., and Smolík, J.. 2016. “We Hate Them All? Issue Adaptation of Extreme Right Parties in Slovakia.” Communist and Post-Communist Studies 49(4):335–344.Google Scholar
Klüver, H., and Spoon, J.-J.. 2016. “Who Responds? Voters, Parties and Issue Attention.” British Journal of Political Science 46(3):633–654.Google Scholar
Krippendorff, K. 2004. Content Analysis: An Introduction to Its Methodology , 2nd edn. Thousand Oaks, CA: Sage Publications.Google Scholar
Kullback, S., and Leibler, R. A.. 1951. “On Information and Sufficiency.” The Annals of Mathematical Statistics 22(1):79–86.Google Scholar
Le, Q., and Mikolov, T.. 2014. “Distributed Representations of Sentences and Documents.” In Proceedings of the 31st International Conference on Machine Learning (ICML-14) , 1188–1196. http://proceedings.mlr.press/v32/le14.pdf.Google Scholar
Leskovec, J., Backstrom, L., and Kleinberg, J.. 2009. “Meme-tracking and the Dynamics of the News Cycle.” In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 497–506. New York: ACM. http://doi.acm.org/10.1145/1557019.1557077.Google Scholar
Liu, J., Chen, J., and Ye, J.. 2009a. “Large-scale Sparse Logistic Regression.” In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’09 , 547–555. New York: ACM. http://www.scopus.com/inward/record.url?eid=2-s2.0-70350663114&partnerID=tZOtx3y1.Google Scholar
Liu, J., Ji, S., and Ye, J.. 2009b. “ $\{\text{SLEP}\}$ : Sparse Learning with Efficient Projections.” http://yelab.net/software/SLEP/.Google Scholar
McCallum, A. K.2002. “MALLET: A Machine Learning for Language Toolkit.” http://mallet.cs.umass.edu.Google Scholar
Mclachlan, G. J., Do, K.-A., and Ambroise, C.. 2004. Analyzing Microarray Gene Expression Data, vol. 422 . Hoboken, NJ: John Wiley and Sons.Google Scholar
Meguid, B. 2008. Party Competition Between Unequals . New York: Cambridge University Press.Google Scholar
Mesežnikov, G., Gyárfášová, O., and Smilov, D.. 2008. “Populist Politics and Liberal Democracy in Central and Eastern Europe.” IVO (IPA) Working Paper Series, Bratislava.Google Scholar
Mikolov, T., Chen, K., Corrado, G., and Dean, J.. 2013. “Efficient Estimation of Word Representations in Vector Space.” Preprint, arXiv:1301.3781.Google Scholar
Monroe, B. L., Colaresi, M. P., and Quinn, K. M.. 2008. “Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict.” Political Analysis 16(4 SPEC. ISS):372–403.Google Scholar
Perry, J. W., Kent, A., and Berry, M. M.. 1955. “Machine Literature Searching X. Machine Language; Factors Underlying Its Design and Development.” American Documentation 6(4):242–254.Google Scholar
Pukelsheim, F. 1994. “The Three Sigma Rule.” The American Statistician 48(2):88–91.Google Scholar
Rehm, P., and Kitschelt, H.. 2015. “Party Alignment: Change and Continuity.” In The Politics of Advanced Capitalism , edited by Beramendi, P., Hausermann, S., Kitschelt, H., and Kriesi, H., 179–201. New York: Cambridge University Press.Google Scholar
Rehm, P., and Kitschelt, H.. 2018. “Determinants of Dimension Dominance.” In Welfare Democracies and Party Politics: Explaining Electoral Dynamics in Times of Changing Welfare Capitalism , edited by Manow, P., Palier, B., and Schwander, H., 61–88. Oxford: Oxford University Press.Google Scholar
Sartori, G. 1976. Party and Party Systems . New York: Cambridge University Press.Google Scholar
Seghouane, A. K., and Amari, S. I.. 2007. “The AIC Criterion and Symmetrizing the Kullback-Leibler Divergence.” IEEE Transactions on Neural Networks 18(1):97–106. doi:10.1109/tnn.2006.882813.Google Scholar
Shah, D. V., Watts, M. D., Domke, D., and Fan, D. P.. 2002. “News Framing and Cueing of Issue Regimes: Explaining Clinton’s Public Approval in Spite of Scandal.” Public Opinion Quarterly 66(3):339–370.Google Scholar
Siroky, D. S. 2009. “Navigating Random Forests and Related Advances in Algorithmic Modeling.” Statist. Surv. 3:147–163.Google Scholar
Spies, D., and Franzmann, S. T.. 2011. “A Two-Dimensional Approach to the Political Opportunity Structure of Extreme Right Parties in Western Europe.” West European Politics 34(5):1044–1069.Google Scholar
Spoon, J.-J. 2011. Political Survival of Small Parties in Europe . Ann Arbor: University of Michigan Press.Google Scholar
Theocharis, Y., Barber, P., Fazekas, Z., Popa, S. A., and Parnet, O.. 2016. “A Bad Workman Blames his Tweets: the Consequences of Citizens’ Uncivil Twitter use When Interacting with Party Candidates.” Journal of Communication 66(6):1007–1031.Google Scholar
Tucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., and Nyhan, B.. 2018. Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature . Hewlett Foundation. Available at https://www.hewlett.org/wp-content/uploads/2018/03/Social-Media-Political-Polarization-and-Political-Disinformation-Literature-Review.pdf.Google Scholar
Supplementary material: File

Bustikova et al. supplementary material

Bustikova et al. supplementary material 1

Download Bustikova et al. supplementary material(File)
File 850.7 KB