GETAE: Graph information Enhanced deep neural NeTwork ensemble ArchitecturE for fake news detection

TL;DR

GETAE addresses fake news detection by jointly modeling textual content and information diffusion in social networks. It introduces a dual-branch architecture: a Text Branch for rich textual embeddings and a Propagation Branch for diffusion-aware node embeddings, whose outputs are fused into a Propagation-Enhanced Content Embedding for classification. Through extensive experiments on Twitter15 and Twitter16, using Word2Vec, BERT/BERTweet, Node2Vec, and DeepWalk with various recurrent units, GETAE achieves competitive or superior performance compared to state-of-the-art baselines, especially in F1 and accuracy when diffusion context is leveraged. The work demonstrates the practical value of incorporating network diffusion in fake news detection, while acknowledging that no single model suffices and suggesting future directions such as Mixture of Experts and larger multi-modal datasets to further improve robustness and generalization.

Abstract

In today's digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate 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, \underline{G}raph Information \underline{E}nhanced Deep Neural Ne\underline{t}work Ensemble \underline{A}rchitectur\underline{E} 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 uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks (\textsc{[Bi]RNN}) for learning novel contextual features and creating a novel Text Content Embedding. 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 Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel \textit{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 using this approach improves fake news detection and outperforms state-of-the-art models.

Figures (7)

• Figure 1: GETAE Architecture
• Figure 2: Class distribution
• Figure 3: Length distribution
• Figure 4: Number of words distribution
• Figure 5: Examples of propagation trees