Estimating the outcome of UKs referendum on EU membership using e-petition data and machine learning algorithms

Abstract

The United Kingdom’s 2016 referendum on membership of the European Union is perhaps one of the most important recent electoral events in the UK. This political sentiment has confounded pollsters, media commentators and academics alike, and has challenged elected Members of the Westminster Parliament. Unfortunately, for many areas of the UK this referendum outcome is not known for Westminster Parliamentary Constituencies, rather it is known for the coarser geography of counting areas. This study uses novel data and machine learning algorithms to estimate the Leave vote percentage for these constituencies. The results are seen to correlate well with other estimates.

Metadata

Authors/Creators:

Clark, SD https://orcid.org/0000-0003-4090-6002
Morris, M https://orcid.org/0000-0002-9325-619X
Lomax, N https://orcid.org/0000-0001-9504-7570

© 2018 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Keywords:

EU referendum; e-petitions; estimation; machine learning

Dates: