that it is difﬁcult to determine whether one of the gated RNNs
(LSTM, GRU) is more successful, and they are usually cho-
sen based on the basis of the available computing resources.
Girgis et al. [96] experimented with CNN, LSTM, Vanilla,
and GRU. Vanilla suffers from a gradient vanishing problem,
but GRU solves this issue. Though GRU is said to be the
best outcome of their studies, it takes more training time.
A bidirectional GRU was utilized by Singhania et al. [87]
for word-by-word annotation. With preceding and subsequent
words, it captures the word’s meaning within the sentence.
A study by Shu et al. [100] proposed a sentence-comment
co-attention subnetwork model named dEFEND (Explain-
able fake news detection) utilizing news content and user
comments for fake news detection. The authors considered
textual information with bidirectional GRU (Bi-GRU) to
achieve better performance. Moreover, the model has a low
learning efﬁciency.
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning