Citizen Participation and Machine Learning for a Better Democracy

TL;DR

This paper explores how Natural Language Processing and machine learning can enhance digital citizen participation platforms by addressing information overload and improving user experience in democratic decision-making.

Contribution

It demonstrates the application of NLP and machine learning techniques to improve citizen engagement and interaction on the Decide Madrid platform, a novel approach in e-democracy.

Findings

NLP techniques can suggest relevant proposals to citizens.

Machine learning can group citizens by interests effectively.

Summarization of comments improves understanding of public opinion.

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.