What, When and Where of petitions submitted to the UK Government during a time of chaos

TL;DR

This study analyzes UK government petitions from 2015-2017 using NLP to uncover issue trends, geographic support patterns, and their relation to external events like the 2016 referendum.

Contribution

It introduces a ground-up NLP approach to analyze petition issues, temporal dynamics, and geographic support, validated against survey data.

Findings

Some issues are stable over time, others fluctuate with external events.

Petition support varies geographically, with some issues being nationwide and others local.

Six constituency clusters are identified based on petition issue support.

Abstract

In times marked by political turbulence and uncertainty, as well as increasing divisiveness and hyperpartisanship, Governments need to use every tool at their disposal to understand and respond to the concerns of their citizens. We study issues raised by the UK public to the Government during 2015-2017 (surrounding the UK EU-membership referendum), mining public opinion from a dataset of 10,950 petitions (representing 30.5 million signatures). We extract the main issues with a ground-up natural language processing (NLP) method, latent Dirichlet allocation (LDA). We then investigate their temporal dynamics and geographic features. We show that whilst the popularity of some issues is stable across the two years, others are highly influenced by external events, such as the referendum in June 2016. We also study the relationship between petitions' issues and where their signatories are geographically located. We show that some issues receive support from across the whole country but others are far more local. We then identify six distinct clusters of constituencies based on the issues which constituents sign. Finally, we validate our approach by comparing the petitions' issues with the top issues reported in Ipsos MORI survey data. These results show the huge power of computationally analyzing petitions to understand not only what issues citizens are concerned about but also when and from where.