# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Combining Fake Characteristics with Dispersion Information Using GCN  
In an attempt to combine the power of GCNs to represent and capture the structural dimension of fake news and the ability of Recursive Neural Networks (RNNs) to model the way news is spread on social media, [PERSON] et al. [38] proposed a Bi-Directional GCN to explore the propagation and dispersion characteristics of rumors in tandem. Consequently, they model the propagation and dispersion chains of posts using a graph, with the original post and all the subsequent posts related to it as vertices, and directed edges between posts that represents the response relation between a post and any other post that refers to it (or it refers to). The original post and consequently the resulting graphs are labeled as False or True rumors. The authors also applied the DropEdge technique to reduce the  
Figure 4: An overview of the spammer detection process using GCN. over-fitting of their GCNs. The high-level node representations from the two GCNs are concatenated along with the hidden feature vector of the root node using mean-pooling operators. Several full connection layers and a softmax layer are then employed to predict the label of the event. In a similar approach, [PERSON] and [PERSON] [39] construct the social information graph, which consists of vertices that correspond: (i) to political users of Twitter, (ii) other Twitter users that spread content and (iii) news items that are shared by them. The edges of the graph correspond to follower relations between political users and simple users that follow them and post relations between Twitter users and the news items they share. They evaluate both first-order graph embeddings that capture the direct relationships between the graph nodes of different types but also employ a two-layer GCN for node classification. This allows them to classify news items as fake or not using the social representation and the resulting embedding from the GCN. The authors in [40] attempt to represent rumor propagation using a dynamic graph and consequently propose a Dynamic GCN approach for detecting rumors in social media. In their approach, they take graph snapshots and perform representation learning using an attention mechanism that captures both structural and temporal information of rumor diffusion. The proposed method captures the evolving pattern of the rumor diffusion using a series of snapshots of the same propagation graph (actually a tree) that has posts (and post feature vectors) as nodes and edges that denote the response of a post from another post. Sequential snapshots comprising an increasing number of nodes and edges in each step or temporal snapshots that contain a varying number of additional vertices and edges between snapshots are both examined. A two-layer bi-directional GCN with ReLU as an activation function and a global graph pooling (mean pooling) layer is used to convert node representation to graph the representation, and an additive (or dot-product) attention mechanism is employed to retrieve a global graph embedding that re-weighs the graph snapshot embeddings. The output graph embedding is fed to a multi-layer perceptron for classifying the propagation graph as rumor-related or not. In the same direction, the authors in [41] create a reply tree and a user graph based on the originally posted item (tweet) and the items that respond to it (reply tweets) and employ GCNs to detect rumor conversations. Both the reply tree and the user graph have directed edges based on the timestamp of tweets and their nodes are tweets and Twitter users, respectively. The user nodes are characterized by features such as the number of followers and friends, the number of user tweets liked by others, the user's verified identity on Twitter, the user's profile description, etc. The high-frequency words of the source tweets and the propagation time interval between the reply tweets and the source tweet are the two features in the nodes of the reply tree.