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.

在本研究中，我们提出了一种立场感知的图神经网络（stance-aware GNN），利用用户立场主动预测信息传播，通过定制四条信息传递路径以及可训练的注意力权重来提供解释性。通过在真实数据集上评估，立场感知的 GNN 在击败基准测试 32.65％之外，并且在没有用户立场的先进 GNN 上超过 4.69％。此外，注意力权重表明相比支持立场，用户的反对立场对其邻居的行为有更大的影响，起到了社交纠正的作用来阻止信息传播。总的来说，我们的研究为平台提供了一种有效的预测模型来对抗错误信息，并强调用户立场在错误信息传播中的影响。