This study presents an analysis of digital polarization on the topic of the Hijab by examining YouTube comments in Arabic. Employing a novel dataset of around 10K annotated comments, this research investigates the digital discourse using seven labels: Stance, Use of Sarcasm, Argumentation, Cordiality, Offensiveness, Hopefulness, and Apparent Gender of Commenters. The findings reveal significant insights into gender dynamics and the prevalence of specific rhetorical strategies within the debate. This study contributes to the broader field of polarization and argument mining, offering a unique lens on the intersection of digital culture and societal issues in the Arab context.

In this study, we present the first large dataset, ThatiAR, for subjectivity detection in Arabic, consisting of ~3.6K manually annotated sentences, and GPT-4o based explanations. In addition, we include instructions (both in English and Arabic) to facilitate LLM based fine-tuning. We provide an in-depth analysis of the dataset, annotation process, and extensive benchmark results, including PLMs and LLMs. Our analysis of the annotation process highlights that annotators were strongly influenced by their political, cultural, and religious backgrounds, especially at the beginning of the annotation process. The experimental results suggest that LLMs with in-context learning provide better performance. We release the dataset and resources to the community.