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    <titleInfo>
        <title>Leveraging political alignment information for stance detection</title>
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            <title>Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1</title>
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        <name type="personal">
            <namePart type="given">Marlo</namePart>
            <namePart type="family">Souza</namePart>
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            <namePart type="given">Iria</namePart>
            <namePart type="family">de-Dios-Flores</namePart>
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        <name type="personal">
            <namePart type="given">Diana</namePart>
            <namePart type="family">Santos</namePart>
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        <name type="personal">
            <namePart type="given">Larissa</namePart>
            <namePart type="family">Freitas</namePart>
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            <namePart type="given">Jackson</namePart>
            <namePart type="given">Wilke</namePart>
            <namePart type="given">da</namePart>
            <namePart type="given">Cruz</namePart>
            <namePart type="family">Souza</namePart>
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            <namePart type="given">Eugénio</namePart>
            <namePart type="family">Ribeiro</namePart>
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    <abstract>Stance detection is the task of determining whether an input text expresses a stance in favour of or against a given target topic. This, in a standard supervised fashion, will typically require a new set of labelled training examples for each test topic. As an alternative to full supervision (or costly LLM-based methods), this study leverages political alignment information by assuming that stances on related moral or political issues tend to co-occur (e.g., support for a right-wing politician correlating with support for the death penalty or opposition to abortion). This alignment, presently treated as a form of distance labelling, enables stance inference without constructing new corpora and is evaluated against standard cross-domain and prompt-based methods using a large corpus of stances in the Portuguese language.</abstract>
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%0 Conference Proceedings
%T Leveraging political alignment information for stance detection
%A Pavan, Matheus Camasmie
%A Paraboni, Ivandré
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F pavan-paraboni-2026-leveraging
%X Stance detection is the task of determining whether an input text expresses a stance in favour of or against a given target topic. This, in a standard supervised fashion, will typically require a new set of labelled training examples for each test topic. As an alternative to full supervision (or costly LLM-based methods), this study leverages political alignment information by assuming that stances on related moral or political issues tend to co-occur (e.g., support for a right-wing politician correlating with support for the death penalty or opposition to abortion). This alignment, presently treated as a form of distance labelling, enables stance inference without constructing new corpora and is evaluated against standard cross-domain and prompt-based methods using a large corpus of stances in the Portuguese language.
%U https://aclanthology.org/2026.propor-1.3/
%P 20-29

Markdown (Informal)

[Leveraging political alignment information for stance detection](https://aclanthology.org/2026.propor-1.3/) (Pavan & Paraboni, PROPOR 2026)

• Leveraging political alignment information for stance detection (Pavan & Paraboni, PROPOR 2026)

ACL

• Matheus Camasmie Pavan and Ivandré Paraboni. 2026. Leveraging political alignment information for stance detection. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 1, pages 20–29, Salvador, Brazil. Association for Computational Linguistics.