Aaron at SemEval-2026 Task 9: Multilingual Polarization Detection using Transformer-Based Models with Class Weighting and Threshold Tuning
Résumé
This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization.We address all three subtasks: binary polarization detection, polarization type classification, and manifestation identification for English and Swahili.Our approach leverages transformer-based models (RoBERTa-base for English, AfroXLMR-base for Swahili) with class-weighted loss functions to address severe label imbalance and per-label threshold tuning to optimize multi-label classification.On the test set, we achieve F1 macro scores of 0.7901 (English) and 0.7910 (Swahili) for Subtask 1, 0.4615 (English) and 0.4808 (Swahili) for Subtask 2 and 0.4791 (English) and 0.5830 (Swahili) for Subtask 3, which give competitive performance on the leaderboard, demonstrating the effectiveness of our methods for handling imbalanced multi-label polarization detection.Our error analysis reveals that models struggle with dehumanization detection and lack of empathy.
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