Misinformation mitigation in online social networks using continual learning with graph neural networks

In today’s digital landscape, online social networks (OSNs) facilitate rapid information dissemination. However, they also serve as conduits for misinformation, leading to severe real-world consequences such as public panic, social unrest, and the erosion of institutional trust. Existing rumor influence minimization strategies predominantly rely on static models or specific diffusion mechanisms, restricting their ability to dynamically adapt to the evolving nature of misinformation. To address this gap, this paper proposes a novel misinformation influence mitigation framework that integrates Graph Neural Networks (GNNs) with continual learning and employs a Node Blocking strategy as its intervention approach. The framework comprises three key components: (1) a Dataset Generator, (2) a GNN Model Trainer, and (3) an Influential Node Identifier. Given the scarcity of real-world data on misinformation propagation, the first component simulates misinformation diffusion processes within social networks, leveraging the Human Individual and Social Behavior (HISB) model as a case study. The second component employs GNNs to learn from these synthetic datasets and predict the most influential nodes susceptible to misinformation. Subsequently, these nodes are strategically targeted and blocked to minimize further misinformation spread. Finally, the continual learning mechanism ensures the model dynamically adapts to evolving network structures and propagation patterns. Beyond evaluating the Human Individual and Social Behavior (HISB) propagation model, we empirically demonstrate that our framework is propagation-model agnostic by reproducing the pipeline under Independent Cascade and Linear Threshold with consistent gains over baselines. Finally, we introduce a truth-aware intervention rule that gates and weights actions by an external veracity score at detection time, selecting most influential nodes. This addition ensures interventions are enacted only when content is likely false, aligning the method with responsible deployment. Experimental evaluations conducted on multiple benchmark datasets demonstrate the superiority of the proposed node blocking framework over state-of-the-art methods. Our results indicate a statistically significant reduction in misinformation spread, with non-parametric statistical tests yielding p-values below 0.001 (p<0.001), confirming the robustness of our approach. This work presents a scalable and adaptable solution for misinformation containment, contributing to the development of more reliable and trustworthy online information ecosystems.