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Text Mining from Party Manifestos to Support the Design of Online Voting Advice Applications


Buryakov, Daniil; Hino, Airo; Kovacs, Mate; Serdült, Uwe (2022). Text Mining from Party Manifestos to Support the Design of Online Voting Advice Applications. In: 2022 9th International Conference on Behavioural and Social Computing (BESC), Matsuyama, Japan, 29 October 2022 - 31 October 2022. IEEE, 1-7.

Abstract

Voting advice applications (VAA) allow potential voters to compare their own policy positions to political parties running for an election. One of the key design elements of a VAA are the policy statements representing the political space covered by political parties. VAA designers face the challenge of coming up with policy statements in a short time frame. Even with medium-sized corpora of texts such as party manifestos, the formulation and selection of policy statements serving as a stimulus in the VAA is a tedious and time-consuming task. In addition, there is the risk of human selection bias. This study proposes a system to aid VAA designers in policy statement selection and formulation. The system uses the BERT language model with semantic similarity calculation to mine party manifesto sentences that are relevant to already existing VAA statements. For the experiments, VAA statements stemming from the 2021 elections and party manifestos issued for the previous two Japanese elections were used. To expand the policy space, VAA statements from the 2019 European Parliament elections were added. Results show that the proposed system is able to analyze large amounts of text in a short time, and mines text that provides practical support for designing and improving VAAs.

Abstract

Voting advice applications (VAA) allow potential voters to compare their own policy positions to political parties running for an election. One of the key design elements of a VAA are the policy statements representing the political space covered by political parties. VAA designers face the challenge of coming up with policy statements in a short time frame. Even with medium-sized corpora of texts such as party manifestos, the formulation and selection of policy statements serving as a stimulus in the VAA is a tedious and time-consuming task. In addition, there is the risk of human selection bias. This study proposes a system to aid VAA designers in policy statement selection and formulation. The system uses the BERT language model with semantic similarity calculation to mine party manifesto sentences that are relevant to already existing VAA statements. For the experiments, VAA statements stemming from the 2021 elections and party manifestos issued for the previous two Japanese elections were used. To expand the policy space, VAA statements from the 2019 European Parliament elections were added. Results show that the proposed system is able to analyze large amounts of text in a short time, and mines text that provides practical support for designing and improving VAAs.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:02 Faculty of Law > Centre for Democracy Studies Aarau (C2D)
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:340 Law
Language:English
Event End Date:31 October 2022
Deposited On:09 Jan 2023 06:58
Last Modified:29 Jun 2023 00:36
Publisher:IEEE
OA Status:Green
Publisher DOI:https://doi.org/10.1109/besc57393.2022.9995398
  • Content: Published Version
  • Language: English