Making Computers Understand Coalition and Opposition in Parliamentary Democracy

  • Matthias Steinbauer
  • Markus Hiesmair
  • Gabriele Anderst-Kotsis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9820)


In recent years a tremendous raise in the establishment of Open Data initiatives can be observed, aiming at more transparency in government and public institutions. One facet of this trend are data from legislative bodies, including records and archived transcripts of plenary sessions as a measure of transparency and accountability. In this paper the system design and a prototypical implementation of an information system that makes use of these data is presented. From session transcripts naive metrics such as when and how often representatives participate in political discourse but also network metrics as in with whom representatives engage in consenting and opposing discourse can be derived. The objective of the system is to make those relationships visible and accessible to the user in an intuitive way. The system neither can nor attempts to interpret the data, this is left to the user. This paper discusses how data analytics, data visualisation, and network analytics can be facilitated to make the transcripts of legislative bodies more accessible for this purpose. The findings are underpinned by first observations over a proof-of-concept prototype which exploits data available from the Austrian parliament.


Data visualisation Open data Network analytics 


  1. 1.
    Lucioni, R.: Senate Voting Relationships. Private Blog, December 2013. Accessed 8 Mar 2016
  2. 2.
    Krumboltz, M.: The Splitting of the Senate (Now in Convenient GIF Form). Yahoo News, November 2013. Accessed 8 Mar 2016
  3. 3.
    United States of Amoeba: The Economist. Print 7th edn. December 2013Google Scholar
  4. 4.
    GovTrack: Civic Impulse LCC. Accessed 8 Mar 2016
  5. 5.
    Newman, M., Warmbrand, C.: A network analysis of committees in the U.S. House of Representatives. Proc. Natl. Acad. Sci. 102(20), 7057–7062 (2005)CrossRefGoogle Scholar
  6. 6.
    Amelio, A., Pizzuti, C.: Analyzing voting behavior in Italian parliament: group cohesion and evolution. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 140–146 (2012)Google Scholar
  7. 7.
    Jacomy, M., Heymann, S., Tommaso, V., Mathieu, B.: ForceAtlas2, a graph layout algorithm for handy network visualization, TR, gephi consortium (2011).
  8. 8.
    Janssen, M., Charalabidis, Y., Zuiderwijk, A.: Benefits, adoption barriers and myths of open data and open government. Inf. Syst. Manag. (ISM) 29(4), 258–268 (2012)CrossRefGoogle Scholar
  9. 9.
    Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H., Hall, W., schraefel, M.C.: Linked open government data: lessons from IEEE Intell. Syst. 27(3), 16–24 (2012)CrossRefGoogle Scholar
  10. 10.
    Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Record 42(2), 17–28 (2013)CrossRefGoogle Scholar
  11. 11.
    Hsu, C.-L., Park, S.J., Park, H.W.: Political discourse among key Twitter users: the case of Sejong City in South Korea. J. Contemp. East. Asia 12(1), 65–79 (2013)CrossRefGoogle Scholar
  12. 12.
    Kushin, M.J., Kitchener, K.: Getting political on social network sites: exploring online political discourse on Facebook. First Monday 14(11) (2009).
  13. 13.
    Bara, J., Weale, A., Bicquelet, A.: Analysing parliamentary debate with computer assistance. Swiss Polit. Sci. Rev. 13(4), 577–605 (2007)CrossRefGoogle Scholar
  14. 14.
    Sudhara, S., Veltri, G.A., Cristianini, N.: Automated analysis of the US presidental elections using big data and network analysis. Big Data Soc. 2, 1–28 (2015)Google Scholar
  15. 15.
    Raghavan, U.N., Albert, R.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar

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© IFIP International Federation for Information Processing 2016

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Authors and Affiliations

  1. 1.Institute of TelecooperationJohannes Kepler University of LinzLinzAustria

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