ral network-based models have outperformed the traditional
models in terms of their performance owing to their excep-
tional feature extraction ability [62]. In contrast, DL sys-
tems can acquire hidden representations from less complex
inputs. The hidden features can be extracted from both the
news content and context varieties. A study by Hiramath and
Deshpande [78] showed that deep neural networks (DNNs)
require less time than other ML-based classiﬁcation algo-
rithms such as logistic regression, random forest (RF), and
SVM, etc. However, DNNs use more memory. Convolutional
neural network (CNN) and recurrent neural network (RNN)
are two broadly utilized ideal models for deep learning in
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning