In today’s digital age, fake news has become a major problem with serious consequences, ranging from social unrest to political upheaval. New methods for detecting and mitigating fake news are required to address this issue. In this work, we propose incorporating contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural NeTwork Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch combines Word and Transformer embeddings with a Deep Neural Network architecture based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) to capture contextual features and generate a Text Content Embedding. This integrated approach allows for a more comprehensive understanding of the textual information. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE’s Ensemble module combines the Text Content and Propagation Embeddings, to create a powerful and unique Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that this approach improves fake news detection and outperforms state-of-the-art models.
