Work

REGLAT at SemEval-2026 Task 9: Enhancing Arabic Online Polarization Detection Using AraBERT and Synonym Replacement Augmentation

Year: 2026

Type: conference-paper

Abstract: In this paper, we present our system, which was submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection) and focuses on binary classification of polarized content in Arabic social media text.To address Arabic linguistic variations, we propose a single-model approach that combines fine-tuned AraBERT with synonym-based data augmentation.On the Arabic bind set, our method achieves a competitive macro F1-score of 0.831 and an accuracy of 0.833.Among the 45 participating teams, our system ranked 11th overall, with a performance gap of 0.018 macro F1 from the top-ranked team (0.8488).The results show that a fine-tuned AraBERT with synonym replacement is a strong, simple, and reproducible baseline that outperforms more complex setups in dealing with Arabic attitude polarization nuances.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.226

Authors: Ahmed Fetouh, Mariam Francies, Nsrin Ashraf, Hamada Nayel, Rahmath Mohammed

Institutions: Benha University, ElSewedy University of Technology, Prince Sattam Bin Abdulaziz University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166873098

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