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.

翻译：尽管社交媒体平台上虚假信息的广泛传播已成为一项紧迫挑战，但现有平台干预措施在遏制其扩散方面成效有限。在本研究中，我们提出了一种立场感知图神经网络（stance-aware GNN），通过挖掘用户立场来主动预测虚假信息传播。由于不同用户立场会形成独特的回音室，我们在立场感知GNN中定制了四种信息传递路径，而可训练的注意力权重通过突出各结构的重要性提供了可解释性。基于真实数据集的评估表明，立场感知GNN的性能较基准模型提升32.65%，且超越未引入用户立场的先进GNN模型超过4.69%。此外，注意力权重显示，用户的反对立场对邻居行为的影响高于支持立场，这种反对立场发挥社会纠正作用以阻止虚假信息传播。总体而言，本研究为平台对抗虚假信息提供了有效的预测模型，并揭示了用户立场在虚假信息传播中的影响力。