# Councils in Action: Automating the Curation of Municipal Governance Data for Research

arXiv: https://arxiv.org/abs/2204.09110 · HTML: https://mlsanity.com/paper/2204.09110/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 6 of 6)

- [Tracking the Newsworthiness of Public Documents](https://mlsanity.com/paper/2311.09734/) (2023)
- [CitiLink: Enhancing Municipal Transparency and Citizen Engagement through Searchable Meeting Minutes](https://mlsanity.com/paper/2601.18374/) (2026)
- [PUBLICSPEAK: Hearing the Public with a Probabilistic Framework in Local Government](https://mlsanity.com/paper/2503.11743/) (2025)
- [VotIE: Information Extraction from Meeting Minutes](https://mlsanity.com/paper/2601.03997/) (2026)
- [NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark](https://mlsanity.com/paper/2602.08162/) (2026)
- [CitiLink-Minutes: A Multilayer Annotated Dataset of Municipal Meeting Minutes](https://mlsanity.com/paper/2602.12137/) (2026)

## Abstract (arXiv)

Large scale comparative research into municipal governance is often prohibitively difficult due to a lack of high-quality data. But, recent advances in speech-to-text algorithms and natural language processing has made it possible to more easily collect and analyze data about municipal governments. In this paper, we introduce an open-source platform, the Council Data Project (CDP), to curate novel datasets for research into municipal governance. The contribution of this work is two-fold: 1. We demonstrate that CDP, as an infrastructure, can be used to assemble reliable comparative data on municipal governance; 2. We provide exploratory analysis of three municipalities to show how CDP data can be used to gain insight into how municipal governments perform over time. We conclude by describing future directions for research on and with CDP such as the development of machine learning models for speaker annotation, outline generation, and named entity recognition for improved linked data.