Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning

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[Submitted on 29 Jun 2026]

Title:Multilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning

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Abstract: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.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2606.30857 [cs.CL]

(or arXiv:2606.30857v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.30857

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Aaron Bundi Anampiu [view email] [v1] Mon, 29 Jun 2026 19:42:52 UTC (90 KB)

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