Information and news collection via social media platforms is just one of their many useful functions. Nonetheless, they can inflict considerable harm because they can quickly propagate misinformation to thousands of users without proof. Several works of research have been explored recently to automatically regulate rumors by mining the text existing on the social media networks using deep learning techniques. This paper conduct a thorough assessment of deep learning techniques for detecting rumors on social media. The goal of this paper is to better understand current trends in the application of deep learning methods to the problem of identifying rumors. This analysis also includes a discussion of the difficulties researchers have encountered and a number of suggestions for further research on the rumor detection technique under scrutiny. This survey is helpful for researchers in the field because it describes in detail the performance matrices, dataset features, and deep learning model used in each work to enhance rumor detection accuracy.

Keywords : Deep Learning; Socialmedia; Stance Detection; Machine Learning, Deep Learning, OSN, Rumour Detection.