Modeling Dynamic Ideological Behavior in Political Networks
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Published
Aug 28, 2019
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Carlos Henrique Gomes Ferreira
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Fabricio Murai Ferreira
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Breno de Sousa Matos
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Jussara Marques de Almeida
http://orcid.org/0000-0001-9107-6884
Abstract
In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. Whereas the former is an example of a highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose a temporal ideological space model, based on temporal vertice embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members' voting patterns. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.
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References
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[58] J. Wiese, J.-K. Min, J. I. Hong, and J. Zimmerman. “"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate Tie Strength”. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing. CSCW ’15.
[59] Z. Yao, Y. Sun, W. Ding, N. Rao, and H. Xiong. “Dynamic Word Embeddings for Evolving Semantic Discovery”. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. WSDM ’18.
[60] H. Yu, C. Hsieh, S. Si, and I. Dhillon. “Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems”. In: 2012 IEEE 12th International Conference on Data Mining. 765–774. doi: 10.1109/ICDM.2012.168.
[61] L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan. “Scalable temporal latent space inference for link prediction in dynamic social networks”. IEEE Transactions on Knowledge and Data Engineering. 28(10): 2765–2777.
[2] B. Ames. The deadlock of democracy in Brazil. University of Michigan Press.
[3] C. Andris, D. Lee, M. J. Hamilton, M. Martino, C. E. Gunning, and J. A. Selden. “The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives”. PLOS ONE. 10(4): 1–14.
[4] N. Arinik, R. Figueiredo, and V. Labatut. “Signed Graph Analysis for the Interpretation of Voting Behavior”. International Workshop on Social Network Analysis and Digital Humanities.
[5] D. Baldassarri and A. Gelman. “Partisans without constraint: Political polarization and trends in American public opinion”. American Journal of Sociology. 114(2): 408–446.
[6] R. Bamler and S. Mandt. “Dynamic Word Embeddings”. In: Proceedings of the 34th International Conference on Machine Learning. Ed. by D. Precup and Y. W. Teh. Vol. 70. Proceedings of Machine Learning Research. PMLR. 380–389.
[7] A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra. “Clustering on the unit hypersphere using von Mises-Fisher distributions”. Journal of Machine Learning Research. 6(Sep): 1345–1382.
[8] BBC. Brazil profile - Timeline. url: http://www.bbc. com/news/world-latin- america- 19359111 (accessed on 05/15/2018).
[9] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. “Fast unfolding of communities in large networks”. Journal of statistical mechanics: theory and experiment. 2008(10).
[10] I. Budge and M. J. Laver. Party policy and government coalitions. Springer.
[11] Q. Cai, L. Ma, M. Gong, and D. Tian. “A survey on network community detection based on evolutionary computation”. International Journal of Bio-Inspired Computation. 8(2): 84–98.
[12] D. Cherepnalkoski, A. Karpf, I. Mozetič, and M. Grčar. “Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities”. PLOS ONE. 11(11): 1–27.
[13] P. Cui, X. Wang, J. Pei, and W. Zhu. “A survey on network embedding”. IEEE Transactions on Knowledge and Data Engineering.
[14] C. Dal Maso, G. Pompa, M. Puliga, G. Riotta, and A. Chessa. “Voting Behavior, Coalitions and Government Strength through a Complex Network Analysis”. PLOS ONE. 9(Dec.): 1–13.
[15] K. Darwish, W. Magdy, and T. Zanouda. “Trump vs. Hillary: What Went Viral During the 2016 US Presidential Election”. In: International Conference on Social Informatics. Springer. 143–161.
[16] M. A. Davenport and J. Romberg. “An Overview of LowRank Matrix Recovery From Incomplete Observations”. IEEE Journal of Selected Topics in Signal Processing. 10(4): 608–622. issn: 1932-4553.
[17] D. Easley and J. Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
[18] V. Eidelman, A. Kornilova, and D. Argyle. “How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level”. In: Proceedings of the 27th International Conference on Computational Linguistics.
[19] M. P. Fiorina and S. J. Abrams. “Political Polarization in the American Public”. Annual Review of Political Science. 11(1): 563–588.
[20] S. Fortunato. “Community detection in graphs”. Physics Reports. 486(3): 75–174. issn: 0370-1573.
[21] P. Goyal, S. R. Chhetri, and A. Canedo. “dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning”. arXiv: 1809.02657 [cs.SI].
[22] P. Goyal, N. Kamra, X. He, and Y. Liu. “DynGEM: Deep Embedding Method for Dynamic Graphs”. In: 3rd International Workshop on Representation Learning for Graphs (ReLiG) at IJCAI ’18.
[23] M. S. Granovetter. “The strength of weak ties”. In: Social networks. Elsevier. 347–367.
[24] A. Grover and J. Leskovec. “node2vec: Scalable feature learning for networks”. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 855–864.
[25] S. Hix, A. Noury, and G. Roland. “Power to the parties: cohesion and competition in the European Parliament, 1979–2001”. British Journal of Political Science. 35(2): 209–234.
[26] X. Huang, J. Li, and X. Hu. “Accelerated attributed network embedding”. In: Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM. 633–641.
[27] J. J. Jones, J. E. Settle, R. M. Bond, C. J. Fariss, C. Marlow, and J. H. Fowler. “Inferring Tie Strength from Online Directed Behavior”. PLOS ONE. 8(Jan.): 1–6.
[28] A. Kornilova, D. Argyle, and V. Eidelman. “Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction”. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Vol. 2. Melbourne, Australia: Association for Computational Linguistics. 510–515.
[29] M. Kusner, Y. Sun, N. Kolkin, and K. Weinberger. “From word embeddings to document distances”. In: International Conference on Machine Learning. 957–966.
[30] B. E. Lauderdale and A. Herzog. “Measuring political positions from legislative speech”. Political Analysis. 24(3): 374–394.
[31] M. Levorato and Y. Frota. “Brazilian Congress structural balance analysis”. Journal of Interdisciplinary Methodologies and Issues in Science. Graphs & Social Systems.
[32] O. Levy and Y. Goldberg. “Neural word embedding as implicit matrix factorization”. In: Advances in neural information processing systems. 2177–2185.
[33] J. Li, H. Dani, X. Hu, J. Tang, Y. Chang, and H. Liu. “Attributed Network Embedding for Learning in a Dynamic Environment”. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. CIKM ’17. ACM. 387–396.
[34] L. v. d. Maaten and G. Hinton. “Visualizing data using t-SNE”. Journal of machine learning research. 9(Nov): 2579–2605.
[35] S. Mahdavi, S. Khoshraftar, and A. An. “dynnode2vec:Scalable Dynamic Network Embedding”. In: Workshopon Advances in High Dimensional Big Data at IEEE BigData ’18
[36] S. Mainwaring and M. S. Shugart. Presidentialism and democracy in Latin America. Cambridge University Press.
[37] T. E. Mann and N. J. Ornstein. It’s even worse than it looks: How the American constitutional system collided with the new politics of extremism. Basic Books.
[38] J. McGee, J. Caverlee, and Z. Cheng. “Location Prediction in Social Media Based on Tie Strength”. In: Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management. CIKM ’13. San Francisco, California, USA: ACM. 459–468. isbn: 978-1- 4503-2263-8.
[39] I. Mendonça, A. Trouve, and A. Fukuda. “Exploring the Importance of Negative Links Through the European Parliament Social Graph”. In: Proceedings of the 2017 International Conference on E-Society, E-Education and E-Technology. ICSET 2017.
[40] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. “Distributed Representations of Words and Phrases and their Compositionality”. In: Advances in Neural Information Processing Systems 26. 3111–3119.
[41] J. Moody and P. J. Mucha. “Portrait of Political Party Polarization”. Network Science. 1(1): 119–121.
[42] M. E. J. Newman. “Modularity and community structure in networks”. Proceedings of the National Academy of Sciences. 103(23): 8577–8582.
[43] G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim. “Continuous-Time Dynamic Network Embeddings”. In: Companion Proceedings of the The Web Conference 2018. WWW ’18. 969–976.
[44] V.-A. Nguyen, J. Boyd-Graber, P. Resnik, and K. Miler. “Tea party in the house: A hierarchical ideal point topic model and its application to Republican legislators in the 112th congress”. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Vol. 1. 1438–1448.
[45] M. Plantié and M. Crampes. “Survey on social community detection”. In: Social media retrieval. Springer. 65– 85.
[46] M. A. Porter, P. J. Mucha, M. E. J. Newman, and C. M. Warmbrand. “A network analysis of committees in the U.S. House of Representatives”. Proceedings of the National Academy of Sciences. 102(20): 7057–7062.
[47] S. A. Rice. “The behavior of legislative groups: a method of measurement”. Political Science Quarterly. 40(1): 60– 72.
[48] G. Rossetti and R. Cazabet. “Community Discovery in Dynamic Networks: A Survey”. ACM Comput. Surv. 51(2).
[49] T. Sakamoto and H. Takikawa. “Cross-national measurement of polarization in political discourse: Analyzing floor debate in the US the Japanese legislatures”. In: Big Data (Big Data), 2017 IEEE International Conference on. IEEE. 3104–3110.
[50] G. Sartori. Parties and party systems: A framework for analysis. ECPR press.
[51] C. E. Shannon. “A mathematical theory of communication”. ACM SIGMOBILE Mobile Computing and Communications Review. 5(1): 3–55.
[52] P. O. S. Vaz de Melo. “How Many Political Parties Should Brazil Have? A Data-Driven Method to Assess and Reduce Fragmentation in Multi-Party Political Systems”. PLOS ONE. 10(10): 1–24.
[53] N. X. Vinh, J. Epps, and J. Bailey. “Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance”. Journal of Machine Learning Research. 11(Oct): 2837–2854.
[54] Vox. Brazil’s political crisis, explained. url: https : / / www. vox. com / 2016 / 4 / 21 / 11451210 / dilma - rousseff - impeachment (accessed on 05/15/2018).
[55] Y. Wang, Y. Feng, Z. Hong, R. Berger, and J. Luo. “How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers”. In: International Conference on Social Informatics. Springer. 440–456.
[56] A. S. Waugh, L. Pei, J. H. Fowler, P. J. Mucha, and M. A. Porter. “Party polarization in Congress: A social networks approach”. arXiv preprint arXiv:0907.3509.
[57] W. Wei and K. M. Carley. “Measuring temporal patterns in dynamic social networks”. ACM Transactions on Knowledge Discovery from Data (TKDD). 10(1): 9.
[58] J. Wiese, J.-K. Min, J. I. Hong, and J. Zimmerman. “"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate Tie Strength”. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing. CSCW ’15.
[59] Z. Yao, Y. Sun, W. Ding, N. Rao, and H. Xiong. “Dynamic Word Embeddings for Evolving Semantic Discovery”. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. WSDM ’18.
[60] H. Yu, C. Hsieh, S. Si, and I. Dhillon. “Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems”. In: 2012 IEEE 12th International Conference on Data Mining. 765–774. doi: 10.1109/ICDM.2012.168.
[61] L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan. “Scalable temporal latent space inference for link prediction in dynamic social networks”. IEEE Transactions on Knowledge and Data Engineering. 28(10): 2765–2777.
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