This paper presents the OZemi team’s submission to SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization.We propose a unified multilingual approach that addresses multiple languages and subtasks efficiently. Our system combines multilingual models with data-level techniques and a class-weighted cross-entropy loss to mitigate data imbalance across languages, subtasks, and categories. Results show consistent performance across languages, achieving macro F1 scores above 70% in most languages for Subtask 1 achieving our highest rank in subtask 1 for Persian (1 out of 44). These results suggest that the proposed framework provides a flexible foundation for multilingual and multi-task polarization analysis.