@inproceedings{nestor-etal-2026-polarizedteam,
    title = "{P}olarized{T}eam at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = "Nestor, Maria  and
      Al Shrafat, Maroan  and
      Pește, Ioana  and
      Gifu, Daniela  and
      Trandab{\u{a}}ț, Diana",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.180/",
    pages = "1391--1397",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents the systems developed for SemEval-2026 Task 9, which targets the detection and categorization of multilingual, multicultural, and multi-event online polarization across 22 languages. To address the challenges posed by linguistic diversity and short, heterogeneous texts, we evaluate several Transformer-based architectures for multilingual polarization detection. Our approach models the task as a multi-label classification problem and incorporates mean pooling for sentence representation, focal loss to mitigate severe label imbalance, and label-wise attention mechanisms to capture polarization-specific linguistic cues. Experimental results show that combining robust multilingual encoders with label-aware modelling substantially improves the detection of polarized content across diverse communities and events"
}