VOLUME 9, 2021

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M. F. Mridha et al.: Comprehensive Review on Fake News Detection With Deep Learning

Datasets focused on news content are publicly available.
On the other hand, datasets based on different textual
features are limited. Thus research utilizing additional
textual features is scarce.

• Instead of a simple classiﬁer, using an ensemble method
produces better results [49]. By constructing an ensem-
ble model with DL and ML algorithms, in which an
LSTM can identify the original article while passing
auxiliary features through a second model can yield
better results [41]. A simpler GRU model performs
better than an LSTM [80]. Therefore, we recommend
combining GRU and CNNs to urge the leading
result.

• Many researchers have achieved high accuracy by using
CNN, LSTM, and ensemble models [42], [64]. SeqGAN
and Deep Belief Network (DBN) were not explored in
this domain. We encourage researchers to experiment
with these models.
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning