# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 6 Discussion  
Table 1 summarizes the methods surveyed in this work, which capitalize on the use of Graph Convolutional Networks, which are broadly divided into three main groups:  
* Text classification approaches that map post words or whole posts as nodes in the graph and use edges to denote their semantic or positional relations (i.e., co-occurrence, posted by similar users.). They consequently classify graphs using the representations learned using GCNs. * User/source classification approaches that map social network accounts to nodes in the graph and use edges to denote their social relations. The GCNs are used to classify sources as fake/bot/spammers or not. * Propagation graph classification approaches that classify the propagation graphs (either the final graph or several snapshots of it) as fake or not using GCNs. The majority of the works are based on assumptions about the underlying publish and propagation models, for example:  
* A fake post employs entities that do not usually appear together in other posts;
* The productivity or re-production rate of a bot or spam account is much higher than that of a normal account;
* The nodes that are near (within the graph) to a spam or rumor producing account, are more affected than the nodes that are far from the node;
* A fake news propagation graph has different characteristics (fanout, density, etc.) from that of a legitimate news one. The methods proposed so far take advantage of the core social network information, employ language models, such as BERT or word embeddings, in order to produce comprehensive text representations, and, in some cases, employ multimodal information to support the classification task. In the case of content classification, images are either processed using parallel deep neural network architectures, and the resulting representations are concatenated with the text representations before being fed to the GCN. In the case of source or propagation graph classification, the emphasis is on features that can enhance the user profile information, such as features from the content of the post, the graph structure or the evolution of the propagation graph over consecutive snapshots. Most of the methods generate homogeneous uni-partite graphs, which either comprises word or user nodes interconnected with edges based on similarity or on social relations, respectively. Some works introduce the use of heterogeneous graphs that comprise both textual nodes (words, concepts or posts) and user nodes who are connected with directed or undirected edges depending on the case. Different edge weighting schemes are employed in order to enhance the graph edges with additional information concerning the strength of node relations, whereas a multitude of features are attached to the graph nodes in an attempt to enrich their representation. The surveyed methods usually stack from two up to four GCN layers in order to capture higher-order neighborhood information in the graph and an additional dense layer to solve the respective classification task. GCNs allow combining the embeddings of each node with those of its first, second or higher-order neighbors. Thus, in the case of heterogeneous networks, such as those that comprise posts and users, it allows learning how the posts of a user's neighbors affect the credibility of the user or how the neighborhood of a user affects the credibility of the content she/he posts. In the case of homogeneous networks with directed edges, GCNs learn how a user is affected by her/his followers or the people that follow them or how the terms or concepts in fake news items affect the veracity of other news that contains the same terms or terms related to them. The learned parameters are the node embeddings and the weights of all neighbors within each layer. As shown in Figure 6, the overall architecture of the GCN comprise one or more graph convolution layers, which are responsible for aggregating information from its neighboring nodes in each node.