Estimating the outcome of UKs referendum on EU membership using e-petition data and machine learning algorithms
Dados Bibliográficos
| ID | 12972174 |
|---|---|
| Autores | Stephen D Clark (0000-0003-4090-6002, University of Leeds, autor correspondente), Michelle Morris (0000-0002-9325-619X, University of Leeds), Nik Lomax (0000-0001-9504-7570, University of Leeds) |
| Ano | 2018 |
| Volume | 15 |
| Fascículo | 4 |
| Páginas | 344-357 |
| Data de publicação | 2018-08-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Journal of Information Technology & Politics (JOURNAL) |
| Identificadores do periódico | ISSN: 1933-169X • E-ISSN: 1933-1681 |
| Editora | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/19331681.2018.1491926 |
| OpenAlex | W2887269678 |
| Idioma | EN |
| Citações recebidas | 6 |
| Referências citadas | 43 |
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
Algorithm · Economic policy · Economics · European union · Mathematical economics · Outcome (game theory · Parliament · Political economy · Political science · Politics · Referendum · Sociology · Bayesian Methods and Mixture Models · Computational and Text Analysis Methods · Computer Science · Data-Driven Disease Surveillance · Law · Public Administration
Introduction. La participation politique en ligne au révélateur du pétitionnement électronique
A machine learning approach to open public comments for policymaking
A worlds-eye view of the United Kingdom through parliamentary e-petitions
Linguistic and semantic factors in government e-petitions
Predicting and explaining corruption across countries
Data collaboration in digital government research
Applied Predictive Modeling
Econometric Computing with HC and HAC Covariance Matrix Estimators
Top 10 algorithms in data mining
Support-Vector Networks
Multivariate Adaptive Regression Splines
Building Predictive Models in R Using the caret Package
A few useful things to know about machine learning
A tutorial on support vector regression
Geographically Weighted Regression
Greedy function approximation
‘Reaching in’? The potential for e-petitions in local government in the United Kingdom
‘Success’ and online political participation
Birds of a feather petition together? Characterizing e-petitioning through the lens of platform data
Assessing (e-)Democratic Innovations
The empiricist’s challenge
The 2016 Referendum, Brexit and the Left Behind
Voting out of the European Union
E-Petitions at Westminster
Do Legislative Petitions Systems Enhance the Relationship between Parliament and Citizen
Examining political mobilization of online communities through e-petitioning behavior in We the People
A Demographic Rationale for Brexit
Britain's changed electoral map in and beyond 2015
Online petitions
| Obras citantes distintas | 6 |
|---|---|
| Citações por ano | 0,86 |
| Intervalo de citações | 2019 - 2025 (7) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 6 |