the users who distributed relevant posts) [5]. User-based fea-
tures are typically from the user proﬁle in social media [98],
[111]. Users’ social responses in terms of stances [42], [64],
topics [112], or credibility [113]–[115] are represented via
post-based features. Recently, several studies have focused
on stance features to detect fake news [64]. It can be effective
for human fact-checkers to distinguish false claims [113],
[114]. To check the authenticity of a claim/report/headline,
it is essential to understand what different news agencies are
declaring about that particular claim/report/headline. Refer-
ence [116]. Features that are network-based are retrieved by
creating specialized networks, such as diffusion networks,
interaction networks, and propagation networks [117]–[119].
The propagation network contains rich information about
user interactions (likes, comments, responses, or shares) that
A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning