# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 7 Conclusions  
The different GCN techniques presented in this article demonstrate the advantages of graph-based representations and GCNs over simple CNNs or RNNs in graph and node classification tasks. The graphs better capture the latent relations between concepts in the news text, between the users that spread the news or between users and the texts they share. The graph convolution allows communicating this information between the graph nodes and capturing the effect of social relations (neighborhood) in the dispersion of fake news. Figure 6: The architecture of GCNs in a generic graph classification task. Finally, temporal graph-convolution techniques manage to better capture the evolution of the news spreading graphs and detect rumors early. On the opposite side of their good performance, GCN-based methods suffer from high complexity, e.g., for the computation of the decomposition of the Laplacian matrix. Even when the eigenvectors are pre-computed or rank approximations of the eigenvalue decomposition are employed, the complexity is high, especially for large-scale graphs. Another major issue that is evident in all works is the large amount of hyper-parameters that must be learned in each case. The methods usually update this parameters in an end-to-end learning manner, which limits their modularity and the ability to reuse certain components of each architecture in a different task. The work in [8] performs an in-depth discussion of GCNs and their scalability and complexity issues. The importance for component reusability is evident in many of the works that employ pre-trained language models (e.g., BERT, XLNet) for text pre-processing or pre-trained CNNs (e.g., VGG-19) to extract visual features. With this in mind, pre-trained graph embeddings, such as the knowledge graph embedding of DGL-KE ([[https://github.com/awslabs/dgl-ke](https://github.com/awslabs/dgl-ke)]([https://github.com/awslabs/dgl-ke](https://github.com/awslabs/dgl-ke)), accessed on 17 February 2022), could be an interesting addition and a useful component for developing graph-based solutions for fake news, spammers or rumor detection. An issue reported in the works surveyed in this study is the difficulty in extracting and handling the visual content of fake news and in associating the visual features with the respective concepts that appear in the textual content. The use of pre-trained models definitely assists feature and concept extraction, but the association of features with concepts still requires training for fine-tuning the different hyper-parameters. Another possible pitfall for the GCN model that employs the early stopping criterion is the need for an additional validation set for model selection. In the absence of a validation dataset, a GCN optimized on the training data will probably only have a sharp drop in performance, especially when the training size is small [62]. Future research in the field has to experiment with more variants of the GCN that incorporate multimodal content information [63] (using word, document and image embeddings [64]), graph structure information (in the form of graph embeddings [65]) and side features for nodes and edges. Global graph pooling techniques [66] or any other structured [67] or hierachical [68] graph pooling method that can capture local properties of the graph formed around a fake news source have to be examined in combination with GCN in order to improve the current state of the art. Finally, all the possible GCN issues [69] must be considered under the prism of the specific fake news and rumor detection tasks. Conceptualization, I.V. and D.M. ; methodology, F.G.; investigation, F.G.; writing--original draft preparation, I.V. and D.M. ; writing--review and editing, I.V. ; supervision, I.V. ; project administration, P.T. ; funding acquisition, P.T. All authors have read and agreed to the published version of the manuscript.