A study by Huang et al. [140] claimed to be the ﬁrst
that experimented using a rich structure of user behavior for
rumor detection. The user encoder uses graph convolutional
networks (GCN) to learn a representation of the user from
a graph created by user behavioral information. The authors
used two recursive neural networks based on tree structure:
bottom-up RvNN encoder and top-down RvNN encoder. The
tree structure is shown in Figure 8. The proposed model
performed worse for the non-rumor class cause user behavior
information brings some interference in non-rumor detection.
Another study by Bian et al. [139] proposed top-down
GCN and bottom-up GCN using a novel method DropEdge
[141] for reducing over-ﬁtting of GCNs. In addition, a
root feature enhancement operation is utilized to improve
the performance of rumor detection. Although it performed
well on three datasets (Weibo, Twitter15, Twitter16), the
outliers in the dataset affected the models’ performance.

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A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning