# Citizen Participation and Machine Learning for a Better Democracy

arXiv: https://arxiv.org/abs/2103.00508 · HTML: https://mlsanity.com/paper/2103.00508/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 98 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Universal Dependency Parsing from Scratch](https://mlsanity.com/paper/1901.10457/) (2019)
- [The Why and How of Nonnegative Matrix Factorization](https://mlsanity.com/paper/1401.5226/) (2014)
- [Stability of Topic Modeling via Matrix Factorization](https://mlsanity.com/paper/1702.07186/) (2017)
- [Information Overload in Group Communication: From Conversation to Cacophony in the Twitch Chat](https://mlsanity.com/paper/1610.06497/) (2016)
- [The Fundamentals of Policy Crowdsourcing](https://mlsanity.com/paper/1802.04143/) (2018)
- [Digital Limits of Government: The Failure of E-Democracy](https://mlsanity.com/paper/1803.06136/) (2018)

## Built on by (most cited first, 14 of 14)

- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide](https://mlsanity.com/paper/2503.07690/) (2025)
- [Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland](https://mlsanity.com/paper/2111.11766/) (2021)
- [Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers](https://mlsanity.com/paper/2410.22937/) (2024)
- [From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces](https://mlsanity.com/paper/2411.00949/) (2024)
- [Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making](https://mlsanity.com/paper/2502.08542/) (2025)
- [Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation](https://mlsanity.com/paper/2409.07780/) (2024)
- [A mixed-methods ethnographic approach to participatory budgeting in Scotland](https://mlsanity.com/paper/2109.09517/) (2021)
- [Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes](https://mlsanity.com/paper/2110.05847/) (2021)
- [Expansive Participatory AI: Supporting Dreaming within Inequitable Institutions](https://mlsanity.com/paper/2211.12434/) (2022)
- [Application of Text Analytics in Public Service Co-Creation: Literature Review and Research Framework](https://mlsanity.com/paper/2305.18316/) (2023)
- [Towards Effective E-Participation of Citizens in the European Union: The Development of AskThePublic](https://mlsanity.com/paper/2504.03287/) (2025)
- [Algorithmic Shortlisting in Participatory Budgeting](https://mlsanity.com/paper/2508.06577/) (2025)
- [VotIE: Information Extraction from Meeting Minutes](https://mlsanity.com/paper/2601.03997/) (2026)

## Abstract (arXiv)

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.