A Proposed Model for Persian Stance Detection on Social Media

Stance detection is a recent research topic that has become an emerging paradigm  of the importance of opinion-mining. It is intended to determine the author’s views toward a specific topic or claim. Stance detection has become an important module in numerous applications such as fake news detection, argument search, claim validation, and author profiling. Despite considerable progress made in this regard in languages like English, unfortunately, we have not made good progress in some languages such as Persian, where we are confronted with a lack of datasets in this area. In this paper, two solutions are used to address this issue: 1) the use of data augmentation and 2) the application of different learning approaches (machine learning, deep learning, and transfer learning) and a meaningful combination of their outcomes. The results show that each of these solutions can not only enhance stance detection performance, but when both are combined, a very significant improvement in the results is achieved.