# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Detecting Fake Sources Using GCNs  
The detection of bot or fake accounts is an important yet challenging task that can significantly help in combating the propagation of fake news in social networks. The main characteristic of such accounts is that they disguise and pretend to be legitimate user accounts and that they usually operate collectively and in bursts. Graph representations and GCNs have proven helpful in the task of correctly detecting such accounts, taking into account the features of the accounts primarily but also information from their neighborhood. [PERSON] et al. [31] combine the user account description, the content that it posts, other numerical and categorical features that they can assign to an account and the information of neighboring accounts in order to distinguish bots among users. They use RoBERTa [32] to encode user description and user posts (tweets). They also attach numerical (e.g., number of followers, followees, likes, etc.) and categorical (e.g., geolocation enabled, contributors enabled, etc.) features available from the Twitter API. Consequently, the users are treated as nodes in a graph, which has two types of edges (i.e., following and follower). By applying Relational Graph Convolutional Networks to the graph, they learn user representations, which are fed to a softmax layer for the binary classification task (i.e., bot or not). Similarly, the authors in [33] propose a bot detection technique that combines social network altmetrics and GCN to distinguish between bots and humans. The unweighted and undirected graph that is the basis of this technique contains Twitter users as nodes and edges that correspond to retweets or mentions. The properties of a user refer to several altmetrics, including degree, triangle count, closeness centrality, etc. A four-layer GCN model and a softmax dense layer are used for classifying the nodes as tweet posted by humans or bots. In a very recent work [34], the authors combine BERT and GCN and propose a transductive learning method for detecting social bots. The method is based on a heterogeneous graph that comprises word and document nodes, which represent the unique words (vocabulary) and documents in the collection. Edges either denote the word occurrence in a document (word-document edges) or word co-occurrence (word-word edges) and are weighted using TF-IDF and PMI, respectively. The resulting graph is fed to a two-tier GCN, and its output is sent to the softmax classifier. An auxiliary classifier is mainly built by embedding the document using BERT and feeding it to a dense layer with softmax activation. The joint optimization of BERT and GCN parameters is carried out by using the cross-entropy loss at the nodes of the markup document. Social spammers are the targets in [35], where the authors model the social network as a graph with both uni- and bi-directional edges and users as nodes. They also replace the forward propagation rule of GCN on a single directed graph (as shown in Equation (1)) with a layer-wise propagation rule that aggregates the propagation over the incoming, outgoing and bi-directional edges of a node. They also assume that spammers have more outgoing neighbors than incoming ones and use a pairwise Markov Random Field (MRF) to model the joint probability distribution of all users' identities. The MRF layer is stacked on top of the modified GCN and the whole model can be trained in an end-to-end manner to correctly classify a node as a spammer user or not. Recently, the authors in [36] proposed a weak supervised learning approach to transform the PHEME dataset into a rumor spreader dataset. They exploited the user-user reply graph that is inherent in this dataset and use sentiment analysis of the tweets to identify rumors (i.e., tweets with negative sentiment in the replies are identified as rumors). The text and user profile features along with some ego-network features of each user constitute the node (user) information of the social graph, which is fed to a GCN in order to classify users as rumor spreaders or not.