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Citizen participation and machine learning for a better democracy

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Arana Catania, Miguel, van Lier, Felix, Procter, Rob, Tkachenko, Nataliya, He, Yulan, Zubiaga, Arkaitz and Liakata, Maria (2021) Citizen participation and machine learning for a better democracy. Digital Government: Research and Practice, 2 (3). pp. 1-22. 27. doi:10.1145/3452118

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Official URL: https://doi.org/10.1145/3452118

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Abstract

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens? experience of digital citizen participation platforms. Taking as a case study the ?Decide Madrid? Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

Item Type: Journal Article
Subjects: J Political Science > JF Political institutions (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Political participation -- Technological innovations, Natural language processing (Computer science) , Human-computer interaction , Machine learning, Artificial intelligence
Journal or Publication Title: Digital Government: Research and Practice
Publisher: ACM
ISSN: 2639-0175
Official Date: 11 July 2021
Dates:
DateEvent
11 July 2021Available
22 February 2021Accepted
Volume: 2
Number: 3
Page Range: pp. 1-22
Article Number: 27
DOI: 10.1145/3452118
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDNational Endowment for Science Technology and the Artshttp://dx.doi.org/10.13039/501100000822
UNSPECIFIEDAlan Turing Institutehttp://dx.doi.org/10.13039/100012338
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