NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification
Year: 2026
Type: conference-paper
Abstract: Detecting polarization in online discourse is important for understanding social fragmentation, yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing.In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2).We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs.For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting.For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves an F1 macro = 0.62.Prompt-language effects are model-and taskdependent, with some settings doing better with English prompts and others with Arabic prompts.Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166819055