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Converting the Outcomes of Citizens’ Discourses in the Cyberspace into Policy Inputs for More Democratic and Effective Government

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Beyond Bureaucracy

Part of the book series: Public Administration and Information Technology ((PAIT,volume 25))

Abstract

Using a real-life and imagined case studies, we demonstrate how a casual, informal political conversation on social media among ordinary citizens could be transformed into a policy discourse. It is done by deconstructing the logic of discursive interactivity of online discussions. In doing so, we apply Jurgen Habermas’ validity claims to normative rightness to reveal citizens’ attitudinal positions ‘For’ and ‘Against’ certain social effects of food destruction policy of the Russian government. We measure citizens’ attitudes in the form of the discursively constructed solidarities behind each position and show the interactive process of their formation. We also build a range of interactivity models that could exploit the potential of artificial neural networks for creating new tools of discourse analytics that can capture citizens’ policy inputs in an easily understood format. The goal of such tools is seen in helping reduce deliberative disagreements by encouraging acceptance of other points of view—the core principle and ideal of deliberative democracy.

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Notes

  1. 1.

    There is also a similarly understood term Computer-mediated conversation (CMC)—see Herring (2011).

  2. 2.

    It is worthwhile noting that the first international conference on online deliberation was organized relatively recently in 2003. http://web.archive.org/web/20060314061609/ http://caae.phil.cmu.edu/style/Seminar.html.

  3. 3.

    Such as, among numerous examples, the IDEA platform debate education http://idebate.org/ or more commercially oriented Citizen Space http://www.citizenspace.com/info on the Delib platform http://www.delib.net/ in the UK and Disqus https://disqus.com/.

  4. 4.

    For example, the main platform of the Russian Government to consult the public on new legal and regulatory acts http://regulation.gov.ru/ almost does not have any citizens’ comments.

  5. 5.

    See, e.g. Google’s open source TensorFlow library of machine learning tools https://www.tensorflow.org and system, Amazon’s deep learning software http://venturebeat.com/2016/05/11/amazon-open-sources-its-own-deep-learning-software-dsstne/, Yahoo’s machine learning datasets https://techcrunch.com/2016/01/14/yahoo-releases-its-biggest-ever-machine-learning-dataset-to-the-research-community/.

  6. 6.

    See e.g. http://www.aclweb.org/anthology.

  7. 7.

    E.g., hidden, presumed intention or implicit proposition in the case of the illocutionary type of speech acts as opposed to the openly and explicitly articulated propositional, meaningful statements described by the locutionary type of speech acts.

  8. 8.

    1.4 million voting sessions organized as of 15 September 2016.

  9. 9.

    The paper is also a good reference source for other discussion facilitation and semantic sense-making tools such as Compendium, Cohere, Debategraph.

  10. 10.

    See more in Chugunov et al. (2016b).

  11. 11.

    President Putin’s Executive Order of July 29, 2015; the embargo was initially set to last until August 6, 2016, but was extended into 2017; see more in Chugunov et al. (2016b).

  12. 12.

    In doing so we applied the Austian linguistic notion of illocutionary force for identifying the intended, implicit meaning contrary to the explicitly formulated locutionary speech acts.

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Authors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Yuri Misnikov & Andrei Chugunov

  2. St. Petersburg State University, St. Petersburg, Russia

    Olga Filatova

Authors
  1. Yuri Misnikov
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  2. Olga Filatova
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  3. Andrei Chugunov
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Correspondence to Yuri Misnikov .

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Editors and Affiliations

  1. Vienna University of Technology, Institute of Computer Aided Automation, Wien, Wien, Austria

    Alois A. Paulin

  2. Project Management Department, Technological Education Inst (TEI), Larissa, Thessaloniki, Greece

    Leonidas G. Anthopoulos

  3. Dept of Public Administration, The Univ of Texas at San Antonio, San Antonio, Texas, USA

    Christopher G. Reddick

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Misnikov, Y., Filatova, O., Chugunov, A. (2017). Converting the Outcomes of Citizens’ Discourses in the Cyberspace into Policy Inputs for More Democratic and Effective Government. In: Paulin, A., Anthopoulos, L., Reddick, C. (eds) Beyond Bureaucracy. Public Administration and Information Technology, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-54142-6_15

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