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EJ: A Free Software Platform for Social Participation

  • Fábio Macêdo MendesEmail author
  • Ricardo Poppi
  • Henrique Parra
  • Bruna Moreira
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 556)

Abstract

As the Internet grows on importance as a forum for political activity, it is necessary to occupy it with proper tools for democratic discussion, dialogue and deliberation. Currently, a substantial part of political debate is conducted on social media inside proprietary networks. Those solutions are flagrantly inadequate to build consensus seeking understandings and to mediate the interaction between the government and the citizenry. This work present EJ, a platform for crowdsourced social participation which uses machine learning based intelligence and gamification techniques to increase engagement and counteract the formation of opinion bubbles and the “echo chamber” effect of social networks.

Keywords

Social participation E-governement Web Free software 

Notes

Acknowledgements

The authors would like to thank Fundação Perseu Abramo and the former Ministry of Human Rights (now transformed into a secretary by the current Brazilian government) for recognizing the importance of direct social participation in shaping public policy and for financial support.

References

  1. 1.
    Albrechtslund, A.: Online social networking as participatory surveillance. First Monday 13(3) (2008).  https://doi.org/10.5210/fm.v13i3.2142, http://journals.uic.edu/ojs/index.php/fm/article/view/2142
  2. 2.
    Barry, E.J., Kemerer, C.F., Slaughter, S.A.: How software process automation affects software evolution: a longitudinal empirical analysis. J. Softw. Maint. Evol.: Res. Pract. 19(1), 1–31 (2007).  https://doi.org/10.1002/smr.342CrossRefGoogle Scholar
  3. 3.
    Best, M.L., Wade, K.W.: The internet and democracy: global catalyst or democratic dud? Bull. Sci. Technol. Soc. 29(4), 255–271 (2009).  https://doi.org/10.1177/0270467609336304CrossRefGoogle Scholar
  4. 4.
    Brabham, D.C.: Crowdsourcing the public participation process for planning projects. Plan. Theory 8(3), 242–262 (2009).  https://doi.org/10.1177/1473095209104824CrossRefGoogle Scholar
  5. 5.
    Cox, T.F., Cox, M.A.A.: Multidimensional scaling. In: No. 88 in Monographs on statistics and applied probability, 2nd edn. Chapman & Hall/CRC, Boca Raton (2001)Google Scholar
  6. 6.
    Del Vicario, M., et al.: The spreading of misinformation online. Proc. Natl. Acad. Sci. 113(3), 554 (2016).  https://doi.org/10.1073/pnas.1517441113CrossRefGoogle Scholar
  7. 7.
    Diamond, L.: Facing up to the democratic recession. J. Democr. 26(1), 141–155 (2015).  https://doi.org/10.1353/jod.2015.0009CrossRefGoogle Scholar
  8. 8.
    Levitsky, S., Way, L.: The rise of competitive authoritarianism. J. Democr. 13(2), 51–65 (2002).  https://doi.org/10.1353/jod.2002.0026CrossRefGoogle Scholar
  9. 9.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    McKinney, W.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference (SCIPY 2010), pp. 51–56 (2010).  https://doi.org/10.1016/S0168-0102(02)00204-3
  11. 11.
    Ogden, M., Mc Kelvey, K., Madsen, M.B., et al.: Dat-distributed dataset synchronization and versioning. Open Sci. Fram. 10 (2017)Google Scholar
  12. 12.
    Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Penguin Books Limited (2011). https://books.google.com.br/books?id=-FWO0puw3nYC
  13. 13.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python (2012).  https://doi.org/10.1007/s13398-014-0173-7.2. http://arxiv.org/abs/1201.0490
  14. 14.
    Piketty, T., Goldhammer, A.: Capital in the twenty-first century (2017). oCLC: 1063105013Google Scholar
  15. 15.
    Salganik, M.J., Levy, K.E.C.: Wiki surveys: open and quantifiable social data collection. PLOS ONE 10(5), e0123483 (2015).  https://doi.org/10.1371/journal.pone.0123483, https://dx.plos.org/10.1371/journal.pone.0123483
  16. 16.
    Sambra, A.V., et al.: Solid: a platform for decentralized social applications based on linked data (2016)Google Scholar
  17. 17.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM, New York (2001).  https://doi.org/10.1145/371920.372071
  18. 18.
    Masson-Delmotte, V., et al.: IPCC. Global warming of 1.5Â\(^\circ \)C. An IPCC special report on the impacts of global warming of 1.5Â\(^\circ \)C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate c, p. 792 (2018).  https://doi.org/10.1017/CBO9781107415324
  19. 19.
    Van Reybrouck, D.: Against Elections (2013)Google Scholar
  20. 20.
    van der Walt, S., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011).  https://doi.org/10.1109/MCSE.2011.37CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Fábio Macêdo Mendes
    • 1
    Email author
  • Ricardo Poppi
    • 1
    • 2
  • Henrique Parra
    • 2
  • Bruna Moreira
    • 1
  1. 1.Universidade de Brasilia (UnB)BrasíliaBrazil
  2. 2.Instituto Cidade DemocráticaSão PauloBrazil

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