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.