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Arguments That Support Decisions in e-Cognocracy: A Qualitative Approach Based on Text Mining Techniques

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5736))

Abstract

E-cognocracy [1-5] is a new democratic system that tries to adequate democracy to needs and challenges of Knowledge Society. This is a cognitive democracy oriented to the extraction and democratization of the knowledge related with the scientific resolution of public decision making problems associated with the governance of society. It is based [3,6] on the evolutionism of live systems and it can be understood as the government of the knowledge and wisdom by means of the information and communication technology (ICT). E-cognocracy combines the representative and the participative democracies by aggregating the preferences of the political parties with those of citizens and by generating knowledge from the conjoint discussion of the arguments that support their own positions. This paper presents a qualitative approach based in text mining tools to identify these arguments from the analysis of the messages and comments elicited by political parties and citizens through a collaborative tool (forum). The proposed methodology has been applied to a case study developed with students of the Faculty of Economics at the University of Zaragoza and related with the potential location at the region of Aragón (Spain), of the greatest leisure project in Europe (Gran Scala).

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© 2009 Springer-Verlag Berlin Heidelberg

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Moreno-Jiménez, J.M., Cardeñosa, J., Gallardo, C. (2009). Arguments That Support Decisions in e-Cognocracy: A Qualitative Approach Based on Text Mining Techniques. In: Lytras, M.D., et al. Visioning and Engineering the Knowledge Society. A Web Science Perspective. WSKS 2009. Lecture Notes in Computer Science(), vol 5736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04754-1_44

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  • DOI: https://doi.org/10.1007/978-3-642-04754-1_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04753-4

  • Online ISBN: 978-3-642-04754-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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