B. RECURRENT NEURAL NETWORK (RNN)
The RNN is a type of neural network. In RNN, nodes are
sequentially connected to construct a directed graph. The
output from the earlier step serves as the input to the cur-
rent step. RNNs are effective in time and sequence-based
predictions. RNN is less compatible with features compared
to CNN. RNNs are suitable for studying sequential texts and
expressions. However, it cannot process very long sequences
when tanh or ReLU is used as an activation function.

The backward-propagation algorithm is utilized in the
RNN for training. While training the neural networks, it is
required to take tiny steps frequently in the way of the nega-
tive error derivative concerning network weights to establish

FIGURE 7. The figure shows an architecture of basic RNN with n
sequential layers. x represents the inputs and y represents the output
generated by the RNN.
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning