a minimum error function. The size of the gradients becomes
tiny for each consequent layer. Thus, the RNN suffers from
a vanishing gradient issue in the bottom layers of the net-
work. We can deal with the vanishing gradient problem by
using three solutions: (1) using rectiﬁed linear unit (ReLU)
activation function, (2) using RMSProp optimization algo-
rithm, and (3) using diverse network architecture such as
long short-term memory networks (LSTM) or gated recur-
rent unit (GRU). So previous studies focused on LSTM and
GRU rather than the state-of-the-art RNN [80], [96], [134].
Bugueño et al. [80] proposed a model based on RNN for
propagation tree classiﬁcation. The authors used RNN for
sequence analysis. The number of epochs was set as 200,
which is relatively high in comparison to their training exam-
ples. To predict fake news articles, authors have proposed
distinctive RNN models, speciﬁcally LSTM, GRU, tanh-
RNN, unidirectional LSTM-RNN, and vanilla RNN. RNNs,
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning