Vincent Etter
ABSTRACT
Switzerland has a long tradition of direct democracy, which makes it an ideal laboratory for research on real-world politics. Similar to recent "open government" initiatives launched worldwide, the Swiss government regularly releases datasets related to state affairs and politics. In this paper, we propose an exploratory, data-driven study of the political landscape of Switzerland, in which we use opinions expressed by candidates and citizens on a web platform during the recent Swiss parliamentary elections, together with fine-grained vote results and parliament votes.
Following this purely data-driven approach, we show that it is possible to uncover interesting patterns that would otherwise require both tedious manual analysis and domain knowledge. In particular, we show that traditional cultural and/or ideological idiosyncrasies can be highlighted and quantified by looking at vote results and pre-election opinions. We propose a technique for comparing the candidates' opinions expressed before the elections with their actual votes cast in the parliament after the elections. This technique spots politicians that do not vote consistently with the opinions that they expressed during the campaign. We also observe that it is possible to predict surprisingly precisely the outcome of nationwide votes, by looking at the outcome in a single, carefully selected municipality. Our work applies to any country where similar data is available; it points to some of the avenues created by user-generated data emerging from open government initiatives, which enable new data-mining approaches to political and social sciences.
- Datacatalogs.org. http://datacatalogs.org/.Google Scholar
- Predikon eigenmap. http://www.predikon.ch/eigenmap.Google Scholar
- Smartvote. http://www.smartvote.ch.Google Scholar
- Stemtest. http://www.stemtest2014.be/.Google Scholar
- Stemwijzer. http://www.stemwijzer.nl/.Google Scholar
- Vote compass. http://votecompass.com/.Google Scholar
- Wahl-o-mat. http://www.bpb.de/politik/wahlen/wahl-o-mat/.Google Scholar
- J. Scott Armstrong and Andreas Graefe. Predicting elections from biographical information about candidates: A test of the index method. Journal of Business Research, 64(7):699--706, 2011.Google ScholarCross Ref
- Joshua Clinton, Simon Jackman, and Douglas Rivers. The statistical analysis of roll call data. American Political Science Review, 98(02):355--370, 2004.Google ScholarCross Ref
- Anthony Downs. An economic theory of political action in a democracy. The Journal of Political Economy, pages 135--150, 1957.Google ScholarCross Ref
- James M. Enelow and Melvin J. Hinich. The spatial theory of voting: An introduction. CUP Archive, 1984.Google Scholar
- Daniel Gayo-Avello. Don't turn social media into another 'literary digest' poll. Communications of the ACM, 54(10):121--128, 2011. Google ScholarDigital Library
- Micha Germann, Fernando Mendez, Uwe Serdult, and Jonathan Wheatley. Exploiting smartvote data for the ideological mapping of swiss political parties. In XXVI Congress of the Italian Political Science Association, pages 13--15, 2012.Google Scholar
- Sean Gerrish and David M. Blei. How they vote: Issue-adjusted models of legislative behavior. In NIPS, pages 2762--2770, 2012.Google Scholar
- Martin Ejnar Hansen and Niels Erik Kaaber Rasmussen. Does running for the same party imply similar policy preferences? evidence from voting advice applications. Representation, 49(2):189--205, 2013.Google ScholarCross Ref
- Sariel Har-Peled. Geometric approximation algorithms. Number 173. American Mathematical Soc., 2011. Google ScholarDigital Library
- 24 heures. Sur smartvote, certains partis aident les candidats à répondre, August 2011. http://goo.gl/Uru3Gt.Google Scholar
- Andreas Ladner, Gabriela Felder, and Jan Fivaz. More than toys? a first assessment of voting advice applications in switzerland. Voting Advice Applications in Europe. The State of the Art, pages 91--123, 2010.Google Scholar
- Andreas Ladner, Jan Fivaz, and Joëlle Pianzola. Voting advice applications and party choice: evidence from smartvote users in switzerland. International Journal of Electronic Governance, 5(3):367--387, 2012.Google ScholarCross Ref
- Daniel Lathrop and Laurel Ruma. Open government: Collaboration, transparency, and participation in practice. O'Reilly Media, Inc., 2010. Google ScholarDigital Library
- Philipp Leimgruber, Dominik Hangartner, and Lucas Leemann. Comparing candidates and citizens in the ideological space. Swiss Political Science Review, 2010.Google ScholarCross Ref
- Roger B. Myerson and Robert J. Weber. A theory of voting equilibria. American Political Science Review, 87(01):102--114, 1993.Google ScholarCross Ref
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825--2830, 2011. Google ScholarDigital Library
- Keith T. Poole. Changing minds' not in congress! Public Choice, 131(3--4):435--451, 2007.Google Scholar
- Keith T. Poole and Howard Rosenthal. A spatial model for legislative roll call analysis. American Journal of Political Science, pages 357--384, 1985.Google Scholar
- Keith T. Poole and Howard Rosenthal. Patterns of congressional voting. American Journal of Political Science, pages 228--278, 1991.Google Scholar
- Erik Tjong Kim Sang and Johan Bos. Predicting the 2011 dutch senate election results with twitter. In Proceedings of SASN 2012, pages 53--60. EACL, 2012. Google ScholarDigital Library
- Jonathon Shlens. A tutorial on principal component analysis. Systems Neurobiology Laboratory, University of California at San Diego, 2005.Google Scholar
- Marko Skoric, Nathaniel Poor, Palakorn Achananuparp, Ee-Peng Lim, and Jing Jiang. Tweets and votes: A study of the 2011 singapore general election. In HICSS 2012, pages 2583--2591. IEEE, 2012. Google ScholarDigital Library
- Lindsay I. Smith. A tutorial on principal components analysis. Cornell University, USA, 51:52, 2002.Google Scholar
- Jaakko Talonen and Mika Sulkava. Analyzing parliamentary elections based on voting advice application data. In IDA X, pages 340--351. Springer, 2011. Google ScholarDigital Library
- Luis Terán, Jan Fivaz, and Stefani Gerber. Using a fuzzy-based cluster algorithm for recommending candidates in e-elections. Fuzzy Methods for Customer Relationship Management and Marketing, page 115, 2012.Google Scholar
- Andranik Tumasjan, Timm Sprenger, Philipp Sandner, and Isabell Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In ICWSM, 2010.Google Scholar
- Kristjan Vassil. Voting Smarter? The Impact of Voting Advice Applications on Political Behavior. PhD thesis, European University Institute, 2011.Google Scholar
- Jonathan Wheatley, Christopher Carman, Fernando Mendez, and James Mitchell. The dimensionality of the scottish political space: Results from an experiment on the 2011 holyrood elections. Party Politics, 2012.Google Scholar
- Wikipedia. Ballot measure, 2013. https://en.wikipedia.org/wiki/Ballot_measure.Google Scholar
Index Terms
- Mining democracy
Recommendations
Understanding Offline Political Systems by Mining Online Political Data
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining"Man is by nature a political animal", as asserted by Aristotle. This political nature manifests itself in the data we produce and the traces we leave online. In this tutorial, we address a number of fundamental issues regarding mining of political data:...
Comments