Combining Social and Government Open Data for Participatory Decision-Making

  • Evangelos Kalampokis
  • Michael Hausenblas
  • Konstantinos Tarabanis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6847)

Abstract

In the last years, several research endeavors were launched aiming at involving popular social media platforms in electronic participation. These early endeavors seem to present some essential limitations related mainly to scalability and uptake. In order to avoid these limitations, we introduce a two-phased approach for supporting participatory decision-making based on the integration and analysis of social and government open data. The proposed approach is based on the literature related to the analysis of massive amounts of social data for future events prediction. In this paper we also present a Web data driven architecture for the implementation of the proposed approach. The architecture is based on the use of linked data paradigm as a layer that will enable integration of data from different sources. We anticipate that the proposed approach will (i) allow decision makers to understand and predict public opinion and reaction about specific decisions; and (ii) enable citizens to inadvertently contribute in decision-making.

Keywords

eParticipation Open government data Social data Linked data Data driven architecture 

References

  1. 1.
    Pitkin, H.F.: The Concept of Representation. University of California Press, Berkeley (1972)Google Scholar
  2. 2.
    Smith, L.G., Nell, C.Y.: FORUM: The Converging Dynamics of Interest Representation in Resources. Environmental Management 21, 139–146 (1997)CrossRefGoogle Scholar
  3. 3.
    Creighton, J.L.: The Public Participation Handbook: Making Better Decisions Through Citizen Involvement. Jossey-Bass, San Francisco (2005)Google Scholar
  4. 4.
    Panopoulou, E., Tambouris, E., Tarabanis, K.: eParticipation Initiatives in Europe: Learning from Practitioners. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 54–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Tambouris, E., Kalampokis, E., Tarabanis, K.: A survey of e-participation research projects in the European Union. International Journal of Electronic Business 6(6), 554–571 (2008)CrossRefGoogle Scholar
  6. 6.
    Sæbø, Ø., Rose, J., Nyvang, T.: The Role of Social Networking Services in eParticipation. In: Macintosh, A., Tambouris, E. (eds.) ePart 2009. LNCS, vol. 5694, pp. 46–55. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ferro, E., Molinari, F.: Making Sense of Gov 2.0 Strategies: No Citizens, No Party. In: Prosser, A., Parycek, P. (eds.) Proceedings of EDEM 2009 (2009)Google Scholar
  8. 8.
    Osimo, D.: Web 2.0 in Government: Why and How? JRC Scientific and Technical Reports. European Commission, Joint Research Centre, Institute for Prospective Technological Studies (2008), http://ftp.jrc.es/EURdoc/JRC45269.pdf
  9. 9.
    Addis, M., Taylor, S., Nasser, B.I., Yoshi, S., Karamagioli, E., Wandhoefer, T., Fallon, F., Fletcher, R., Wilson, C.: New ways for policy-makers to interact with citizens through open social network sites - a report on initial results. In: Internet, Politics, Policy 2010 Conference, Oxford, UK (2010), http://works.bepress.com/timo_wandhoefer/2
  10. 10.
    Charalabidis, Y., Gionis, G., Ferro, E., Loukis, E.: Towards a Systematic Exploitation of Web 2.0 and Simulation Modeling Tools in Public Policy Process. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 1–12. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Hartman, A., Jain, A.N., Ramanathan, J., Ramfos, A., Van der Heuvel, W., Zirpinis, C., Tai, S., Charalabidis, Y., Pasic, A., Johannessen, T., Gronsund, T.: Participatory Design of Public Sector Services. In: Andersen, K.N., Francesconi, E., Grönlund, Å., van Engers, T.M. (eds.) EGOVIS 2010. LNCS, vol. 6267, pp. 219–233. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC (2010) Google Scholar
  13. 13.
    Asur, S., Huberman, B.A.: Predicting the Future With Social Media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 492–499. IEEE Press, Torondo (2010)CrossRefGoogle Scholar
  14. 14.
    Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market. Technical report, arXiv:1010.3003, CoRR (2010) Google Scholar
  15. 15.
    Kalampokis, E., Tambouris, E., Tarabanis, K.: A Classification Scheme for Open Government Data: Towards Linking Decentralized Data. International Journal of Web Engineering and Technology 6(3), 266–285 (2011)CrossRefGoogle Scholar
  16. 16.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data—The Story So Far, Special Issue on Linked Data. International Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)CrossRefGoogle Scholar
  17. 17.
    Hausenblas, M.: Exploiting Linked Data to Build Web Applications. IEEE Internet Computing 13(4), 68–73 (2009)CrossRefGoogle Scholar
  18. 18.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW 2010 19th International Conference on World Wide Web, pp. 851–860. ACM, New York (2010)CrossRefGoogle Scholar
  19. 19.
    Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science 60, 2169–2188 (2009)CrossRefGoogle Scholar
  20. 20.
    Ritterman, J., Osborne, M., Klein, E.: Using Prediction Markets and Twitter to Predict a Swine Flu Pandemic. In: First International Workshop on Mining Social Media, Sevilla, pp. 9–17 (2009)Google Scholar
  21. 21.
    Bothos, E., Apostolou, D., Mentzas, G.: Using Social Media to Predict Future Events with Agent-Based Markets. IEEE Intelligent Systems 25(6), 50–58 (2010)CrossRefGoogle Scholar
  22. 22.
    Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: 28th International Conf. on Human Factors in Computing Systems, pp. 1195–1198. ACM, New York (2010)Google Scholar
  23. 23.
    Cullota, A.: Detecting influenza outbreaks by analyzing Twitter messages, http://arxiv.org/pdf/1007.4748
  24. 24.
    Alonso, J., et al.: Improving Access to Government through Better Use of the Web (2009), http://www.w3.org/TR/2009/NOTE-egov-improving-20090512

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Evangelos Kalampokis
    • 1
    • 2
  • Michael Hausenblas
    • 1
  • Konstantinos Tarabanis
    • 2
  1. 1.Digital Enterprise Research InstituteNational University of IrelandGalwayIreland
  2. 2.Information Systems LabUniversity of MacedoniaThessalonikiGreece

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