Dynamic Rumor Control in Social Networks Using Temporal Graph Neural Networks

The pervasive spread of rumors and misinformation in online platforms demands proactive and efficient methods to detect and mitigate their impact. Our proposed method leverages the temporal dynamics of social network interactions and integrates them into a Temporal Graph Neural Network (TGNN) model for rumor detection and control. The research begins by formulating the mathematical framework of the TGNN model, which includes the representation of social network data, node features, adjacency matrices, and hidden node representations. The TGNN architecture incorporates Temporal Graph Convolution (TGCN) layers to capture temporal dependencies and evolving interactions in the social network, and Temporal Graph Attention (TG-Attention) layers to facilitate edge representations based on hidden node representations. Through a simplified numerical example, we illustrate the effectiveness of the TGNN model in detecting potential rumors and identifying influential nodes likely to spread false information. The model computes rumor probability scores for each node, allowing real-time implementation of rumor control measures, such as targeted interventions, warnings, or counter-speech campaigns. Our findings underscore the significance of considering temporal information in rumor control, enabling more accurate and proactive interventions. Ethical considerations surrounding user privacy, transparency, and biases in control measures are discussed to ensure responsible application.