@inproceedings{arampatzis-arampatzis-2026-duth-semeval-2026,
    title = "{DUTH} at {S}em{E}val-2026 Task 9: Joint Multilingual Fine-Tuning for Online Polarization Detection",
    author = "Arampatzis, Georgios  and
      Arampatzis, Avi",
    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-1.86/",
    pages = "599--604",
    ISBN = "979-8-89176-414-9",
    abstract = "Online polarization on social media presentssubstantial challenges for public discourse, content moderation, and large-scale social analytics across diverse linguistic and cultural contexts. A recent multilingual benchmark enablessystematic evaluation of polarization detectionacross 22 languages and multiple sociopoliticalevents, providing a unified setting for studying socially grounded NLP under multilingualconditions.Wepresent DUTH, a unified multilingual system for binary polarization detection based onjoint fine-tuning of XLM-RoBERTa on the 22languages of SemEval-2026 Task 9 Subtask1. Our system uses a single shared encoderwith a linear classification head and is trainedjointly on the multilingual training set usingmixed-precision optimization. On the officialevaluation, the system achieved an average Accuracy of 0.822 and an average Macro-F1 of0.780 across 22 languages. The results showthat a simple jointly fine-tuned multilingualtransformer provides a competitive and scalable baseline for online polarization detection,while still facing difficulties in implicit, sarcastic, and culturally grounded cases."
}