DUTH at SemEval-2026 Task 9: Joint Multilingual Fine-Tuning for Online Polarization Detection

AbstractOnline 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.