Constructing and evaluating ArabicStanceX: a social media dataset for Arabic stance detection.

Arabic stance detection has attracted significant interest due to the growing importance of social media in shaping public opinion. However, the lack of comprehensive datasets has limited research progress in Arabic Natural Language Processing (NLP). To address this, we introduce ArabicStanceX, a novel and extensive Arabic stance detection dataset sourced from social media, comprising 14,477 tweets across 17 diverse topics. Utilizing the transformer-based MARBERTv2 model, we explore stance detection through Multi-Topic Single Model (MTSM) strategies, achieving a promising F1 score of 0.74 for detecting 'favor' and 'against' stances, and 0.67 overall. Our experiments highlight the model's capabilities and challenges, particularly in accurately classifying neutral stances and generalizing to unseen topics. Further investigations using zero-shot and few-shot learning demonstrate the model's adaptability to new contexts. This study significantly advances Arabic NLP, providing crucial resources and insights into stance detection methodologies and future research directions. The dataset is publicly available.