Stand for Something or Fall for Everything: Predict Misinformation
  Spread with Stance-Aware Graph Neural Networks
Zihan Chen Jingyi Sun Rong Liu Feng Mai
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.