@inproceedings{pavan-paraboni-2026-leveraging,
    title = "Leveraging political alignment information for stance detection",
    author = "Pavan, Matheus Camasmie  and
      Paraboni, Ivandr{\'e}",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.3/",
    pages = "20--29",
    ISBN = "979-8-89176-387-6",
    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."
}