TY - GEN
T1 - Text Mining from Party Manifestos to Support the Design of Online Voting Advice Applications
AU - Buryakov, Daniil
AU - Hino, Airo
AU - Kovacs, Mate
AU - Serdult, Uwe
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - e-democracy
KW - machine learning
KW - natural language processing
KW - voting advice applications
UR - http://www.scopus.com/inward/record.url?scp=85146418646&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146418646&partnerID=8YFLogxK
U2 - 10.1109/BESC57393.2022.9995398
DO - 10.1109/BESC57393.2022.9995398
M3 - Conference contribution
AN - SCOPUS:85146418646
T3 - Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
BT - Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
Y2 - 29 October 2022 through 31 October 2022
ER -