# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Detecting Fake Content Using GCNs  
The approaches that detect fake news from content usually extract textual features from the text of each post or concatenate to them the visual features of any images that are included in the post, consequently training binary classifiers that decide whether the content is fake or not. The authors in [27] proposed a Knowledge-driven Multimodal Graph Convolutional Network (KMGCN) to jointly model the textual and visual information of a post into a unified semantic representation. In a pre-processing step, the text of a post is analyzed using entity linking methods that detect the entities mentioned in the text. The entities are then conceptualized using external knowledge graphs. In the same step, they employ a trained YOLOv3 detector to recognize semantic objects in the associated post images. The labels of these objects are added to the post words, and the respective concepts from the knowledge graph constitute the nodes of the graph (represented using Word2 Vec word embeddings) in the graph construction step. The edges of the graph are constructed based on the PMI of nodes in the documents of the collection. The network that learns the representation vector for each document comprises two GCN layers and a global mean pooling layer that aggregates the vertices of each graph. The representation vector of each post is then fed to a binary classifier. In [28], the authors use word embeddings to fetch the low-dimension representation of a single word and sum-pooling to obtain the fixed-length representation vector for each news item (node features). They also use side information that corresponds to the profile of users that make the posts in order to update the adjacency matrix (i.e., to create edges between news items when they share authors with similar profile features). Instead of stacking multiple GCN layers to merge the long-distance information, they calculate the different distance proximity matrices to describe the correlation between nodes. Consequently, they feed different depth proximity matrices to the GCN and follow the update rule of Equation (1). In a slightly different approach, the graph in [29] is created with words as nodes in an attempt to preserve non-consecutive and long-range dependencies among words and capture structural information at the level of entities. Each post is modeled as three separate graphs, which contain, as nodes, the post words and the respective concepts that result from knowledge conceptualization pre-processing. The graphs comprise a global graph H, a parameterized graph U and an individual graph Q. The edges in graph H are  
Figure 2: The three main directions in the use of GCNs in fake news detection tasks. weighted using the PMI score (word co-occurrence frequency). Graph U is parameterized and optimized during training, and the edges of graph Q are drawn using the normalized embedded Gaussian function to measure the similarity between two words in an embedding space. H, U and Q are added to the final adaptive adjacency matrix, and an adaptive Graph Convolutional Network is trained to exploit the rich semantic relations between words in a data-driven manner (mainly by adjusting the parameters of graph U). The visual features of each post are extracted by feeding images to a separate VGG-19 network and a fully connected layer. Then, a feature-level attention mechanism is employed to learn the correlations between visual and textual content. The concatenated text and visual features constitute the final representation of each post. A fully connected layer activated by a non-linear function is used to classify posts as fake news or not. Finally, the authors in [30] construct a heterogeneous graph with different types of nodes and edges to integrate information about news' relevance in time, content, topic and source and propose a GCN that utilizes a wider receptive field, a neighbor sampling strategy and a hierarchical attention mechanism.