The widespread use of social media has accelerated the dissemination of information, but it has also facilitated the spread of harmful rumors, which can disrupt economies, influence political outcomes, and exacerbate public health crises, such as the COVID-19 pandemic. While graph neural network (GNN)- based approaches have shown significant promise in automated rumor detection, they often lack transparency, making their predictions difficult to interpret. Existing graph explainability techniques fall short in addressing the unique challenges posed by the dependencies among feature dimensions in high-dimensional text embeddings used in GNN-based models. In this article, we introduce contrastive token layerwise relevance propagation (CTLRP), a novel framework designed to enhance the explainability of GNN-based rumor detection. CT-LRP extends current graph explainability methods by providing token-level explanations that offer greater granularity and interpretability. We evaluate the effectiveness of CT-LRP across multiple GNN models trained on three publicly available rumor detection datasets, demonstrating that it consistently produces high-fidelity, meaningful explanations, paving the way for more robust and trustworthy rumor detection systems.

Social media platforms have democratised information creation and dissemination by empowering users worldwide to reach vast audiences almost instantaneously. However, these platforms have also become vectors for spreading misinformation such as rumours. If left unchecked, rumours have the potential to cause great economic and political damage and even worsen public health crises. Automated rumour detection systems are imperative to deal with the volume and velocity of information being exchanged on these platforms. Graph Neural Network (GNN)-based approaches have recently emerged as state-of-the-art (SOTA) in automated rumour detection. Despite their performance, these models remain largely opaque, making explaining their predictions challenging, particularly when dealing with noisy social media data. Existing graph explainability techniques struggle to produce high-fidelity contrastive explanations when presented with such noisy data especially when dealing with a multiclass classification problem. To address the issue of noise susceptibility, we propose a novel framework to maximise both contrastivity and fidelity by reframing the explanation task as a maximum margin optimisation problem. Specifically, we impose constraints on explanation set membership and on the influence difference of the prediction explanation set to each other class explanation set. Extensive experiments on real-world datasets show that the proposed method outperforms SOTA methods.

The rise of social media has enabled individuals to rapidly share information, including rumors, which can have significant impacts on various domains. Traditional approaches to rumor control are impractical for social media platforms due to the volume and speed of information. Automated detection methods are needed that not only identify rumors early but also provide explanations for their decisions to protect free speech. Recent advancements in deep learning have shown promise in automating rumor detection. Graph-based models, such as bidirectional graph convolution network (Bi-GCN), capture propagation, and dispersion patterns to differentiate rumors from the truth. However, the interpretability of these deep learning models is a challenge. This article focuses on graph convolution networks (GCNs), which lack attention maps for easy model attribution but excel at capturing global structural features. We investigate the importance of graph structure in rumor detection using two GCN models on a real-world dataset, analyzing the learned latent propagation and dispersion features. To the best of our knowledge, this is the first study to explore GCNs in rumor detection and investigate the significance of graph structure in this task. Our research addresses three primary questions: 1) the primary contributors to GCN-based rumor detection models and their differences across models; 2) the importance of graph structure for accurate predictions in GCN-based models; and 3) the latent propagation and dispersion features learned by GCN-based detection models during the rumor detection process.