@inproceedings{khodaverdian-etal-2026-polispace,
title = "{P}oli{S}pace: Political Opinion Prediction using Shared Latent Space",
author = "Khodaverdian, Johanna D{'}Ciofalo and
Berdoz, Fr{\'e}d{\'e}ric and
Wattenhofer, Roger",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.243/",
pages = "5447--5463",
ISBN = "979-8-89176-439-2",
abstract = "Modeling and predicting political opinions remains a challenging task for AI systems. We introduce PoliSpace, a framework that embeds individuals and political questions into a shared latent space, where vector alignment directly reflects agreement. Individuals are represented as learnable vectors, and questions are encoded via a contrastively fine-tuned language model with individual-specific affine transformations to capture subjective interpretation. This design yields an embedding space that is robust to question rephrasing and sensitive to negation. Evaluated on real-world voting data from Swiss national elections, PoliSpace achieves an 89.81{\%} F1 score on seen opinion prediction for rephrased questions, outperforming prior embedding-based approaches (66.17{\%} F1). We further introduce retrieval and confidence-aware thresholding mechanisms that reach up to 100{\%} F1 at reduced coverage, demonstrating how the learned embedding space can be leveraged to improve prediction reliability. Ablation studies confirm the importance of both individual-specific interpretation modeling and negation-aware encoder fine-tuning. The learned individual embedding space also exhibits clear clustering by political party affiliation, despite no party-label supervision during training, capturing the underlying ideological structure of the Swiss political landscape."
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<abstract>Modeling and predicting political opinions remains a challenging task for AI systems. We introduce PoliSpace, a framework that embeds individuals and political questions into a shared latent space, where vector alignment directly reflects agreement. Individuals are represented as learnable vectors, and questions are encoded via a contrastively fine-tuned language model with individual-specific affine transformations to capture subjective interpretation. This design yields an embedding space that is robust to question rephrasing and sensitive to negation. Evaluated on real-world voting data from Swiss national elections, PoliSpace achieves an 89.81% F1 score on seen opinion prediction for rephrased questions, outperforming prior embedding-based approaches (66.17% F1). We further introduce retrieval and confidence-aware thresholding mechanisms that reach up to 100% F1 at reduced coverage, demonstrating how the learned embedding space can be leveraged to improve prediction reliability. Ablation studies confirm the importance of both individual-specific interpretation modeling and negation-aware encoder fine-tuning. The learned individual embedding space also exhibits clear clustering by political party affiliation, despite no party-label supervision during training, capturing the underlying ideological structure of the Swiss political landscape.</abstract>
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%0 Conference Proceedings
%T PoliSpace: Political Opinion Prediction using Shared Latent Space
%A Khodaverdian, Johanna D’Ciofalo
%A Berdoz, Frédéric
%A Wattenhofer, Roger
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F khodaverdian-etal-2026-polispace
%X Modeling and predicting political opinions remains a challenging task for AI systems. We introduce PoliSpace, a framework that embeds individuals and political questions into a shared latent space, where vector alignment directly reflects agreement. Individuals are represented as learnable vectors, and questions are encoded via a contrastively fine-tuned language model with individual-specific affine transformations to capture subjective interpretation. This design yields an embedding space that is robust to question rephrasing and sensitive to negation. Evaluated on real-world voting data from Swiss national elections, PoliSpace achieves an 89.81% F1 score on seen opinion prediction for rephrased questions, outperforming prior embedding-based approaches (66.17% F1). We further introduce retrieval and confidence-aware thresholding mechanisms that reach up to 100% F1 at reduced coverage, demonstrating how the learned embedding space can be leveraged to improve prediction reliability. Ablation studies confirm the importance of both individual-specific interpretation modeling and negation-aware encoder fine-tuning. The learned individual embedding space also exhibits clear clustering by political party affiliation, despite no party-label supervision during training, capturing the underlying ideological structure of the Swiss political landscape.
%U https://aclanthology.org/2026.aacl-long.243/
%P 5447-5463
Markdown (Informal)
[PoliSpace: Political Opinion Prediction using Shared Latent Space](https://aclanthology.org/2026.aacl-long.243/) (Khodaverdian et al., AACL-IJCNLP 2026)
ACL
- Johanna D’Ciofalo Khodaverdian, Frédéric Berdoz, and Roger Wattenhofer. 2026. PoliSpace: Political Opinion Prediction using Shared Latent Space. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5447–5463, Hengqin, China. Association for Computational Linguistics.