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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
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 | |||||||||
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| Subjects: | J Political Science > JF Political institutions (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| 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 | |||||||||
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| 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: |
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