  Detecting subjectivity in news sentences is crucial for identifying media
bias, enhancing credibility, and combating misinformation by flagging
opinion-based content. It provides insights into public sentiment, empowers
readers to make informed decisions, and encourages critical thinking. While
research has developed methods and systems for this purpose, most efforts have
focused on English and other high-resourced languages. In this study, we
present the first large dataset for subjectivity detection in Arabic,
consisting of ~3.6K manually annotated sentences, and GPT-4o based explanation.
In addition, we included 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 aim to release the dataset and resources for the community.
