Social media platforms are becoming increasingly integrated into daily life, enabling individuals to express their beliefs and perspectives. Stance detection is an automated process for determining the viewpoint of a text on a particular topic; this is in high demand because of the increasing number of texts on social media. Most stance detection research has focused on the English language. Recent efforts have been made to generate datasets for stance detection in languages other than English. However, no comparable initiatives exist in Arabic. This study utilized the MAWQIF dataset and sequential multi-task learning (SMTL), which combines sarcasm detection and sentiment analysis tasks to enhance stance detection performance. In our SMTL, task dependency modeling is employed to establish a flow of information from the sarcasm task to the sentiment task, and then from these two tasks to the stance detection task, ensuring that the stance detection task benefits from the information derived from sarcasm and sentiment. Many experiments have been conducted to investigate the performance of multi-target classifiers in comparison to target-specific classifiers, as well as the impact of training order on the task. State-of-the-art performance is achieved by the multi-target SMTL model, which utilizes a hierarchical task weighting technique. This model was initially trained on the sarcasm task and then further trained on sentiment. The average F1 score on the testing dataset was 88.3%, which was better than the published results. Our study highlights the importance of multi-task learning in stance detection and investigates the relationship between sentiment, sarcasm, and stance.

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