The purpose of stance detection is to identify the author's stance toward a particular topic or claim. Stance detection has become a key component in applications such as fake news detection, claim validation, argument searching, and author profiling. Although significant progress has been made in stance detection in languages such as English, little attention has been paid in some other languages, including Persian. One of the main problems of research in Persian stance detection is the shortage of appropriate Datasets. In this article, to address this problem, we consider Data augmentation, the artificial creation of training Data, which is used to conquer the shortage of Datasets. In this research, we studied several methods of Data augmentation such as EDA, back-translation, and merging source Dataset with similar one in English language. The experimental results indicate that combining the primary Data set with the translation of another Dataset with similar content in another language (for example English) result in a significant improvement in the performance of the model.