Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks

TL;DR

This paper introduces a stance-aware graph neural network that leverages user stances to predict and understand misinformation spread on social media, outperforming existing methods and revealing the influence of opposition stances.

Contribution

The study proposes a novel stance-aware GNN with customized information passing paths and attention mechanisms, improving misinformation prediction and interpretability.

Findings

Stance-aware GNN outperforms benchmarks by 32.65%.

Opposition stances have a greater impact on misinformation spread.

Attention weights reveal the importance of different user stances.

Abstract

Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a real-world dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation.