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Learning VAA: a new method for matching users to parties in voting advice applications

Learning VAA: a new method for matching users to parties in voting advice applications
Learning VAA: a new method for matching users to parties in voting advice applications
Voting Advice Applications (VAAs) aim to increase voters’ political competence by providing them with the closest political party according to their preferences. To do this, VAAs usually compare and aggregate the positions of users and political parties on a set of policy issues by defining a conceptual space and some distance metric on it. In this paper, we argue that the main method for performing the comparison adapts to users’ preferences unsatisfactorily because 1) they use unjustified a priori decisions for weighting policy issues and 2) they employ the same issue-voting space on all policy issues. Some exceptional cases address these issues by providing a community-based recommendation, but often come with lack of interpretability. To fill these gaps, we propose an adaptive algorithm that learns the configuration of the conceptual space from users’ answers. We employ a hybrid VAA that uses expert coding for the party positions and users’ data to adjust the calculation of the distance between users and parties. This new matching method, the Learning VAA, innovates by adjusting the saliency and issue-voting space for every policy issue. We argue and empirically demonstrate that our model fits better the users’ preferences while providing a higher degree of interpretability.
voting advice applications, machine learning, spatial model, e-democracy, election studies, vote choice, issue voting
1745-7289
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f
Padilla, Javier
67acf9fc-6df4-42d0-9aab-a3755fa4e2d5
Chueca, Enrique
b5001116-de03-4f65-b8e5-21866e4e27ee
Romero Moreno, Guillermo
8c2f32d6-b0b5-4563-af22-c08b410b867f
Padilla, Javier
67acf9fc-6df4-42d0-9aab-a3755fa4e2d5
Chueca, Enrique
b5001116-de03-4f65-b8e5-21866e4e27ee

Romero Moreno, Guillermo, Padilla, Javier and Chueca, Enrique (2020) Learning VAA: a new method for matching users to parties in voting advice applications. Journal of Elections, Public Opinion and Parties. (doi:10.1080/17457289.2020.1760282).

Record type: Article

Abstract

Voting Advice Applications (VAAs) aim to increase voters’ political competence by providing them with the closest political party according to their preferences. To do this, VAAs usually compare and aggregate the positions of users and political parties on a set of policy issues by defining a conceptual space and some distance metric on it. In this paper, we argue that the main method for performing the comparison adapts to users’ preferences unsatisfactorily because 1) they use unjustified a priori decisions for weighting policy issues and 2) they employ the same issue-voting space on all policy issues. Some exceptional cases address these issues by providing a community-based recommendation, but often come with lack of interpretability. To fill these gaps, we propose an adaptive algorithm that learns the configuration of the conceptual space from users’ answers. We employ a hybrid VAA that uses expert coding for the party positions and users’ data to adjust the calculation of the distance between users and parties. This new matching method, the Learning VAA, innovates by adjusting the saliency and issue-voting space for every policy issue. We argue and empirically demonstrate that our model fits better the users’ preferences while providing a higher degree of interpretability.

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JEPOP-2018-0141.R2_Proof_hi - Accepted Manuscript
Restricted to Repository staff only until 12 September 2021.
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Accepted/In Press date: 12 March 2020
Published date: 6 May 2020
Keywords: voting advice applications, machine learning, spatial model, e-democracy, election studies, vote choice, issue voting

Identifiers

Local EPrints ID: 440727
URI: http://eprints.soton.ac.uk/id/eprint/440727
ISSN: 1745-7289
PURE UUID: 38a286ba-d964-46b0-ab81-655b1b1669d4
ORCID for Guillermo Romero Moreno: ORCID iD orcid.org/0000-0002-0316-8306

Catalogue record

Date deposited: 14 May 2020 16:32
Last modified: 07 Oct 2020 02:23

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Contributors

Author: Guillermo Romero Moreno ORCID iD
Author: Javier Padilla
Author: Enrique Chueca

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