Preprint
Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks
arXiv (Cornell University)
10/04/2023
DOI: 10.48550/arxiv.2310.02568
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.
Details
- Title: Subtitle
- Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks
- Creators
- Zihan ChenJingyi SunRong LiuFeng Mai
- Resource Type
- Preprint
- Publication Details
- arXiv (Cornell University)
- DOI
- 10.48550/arxiv.2310.02568
- eISSN
- 2331-8422
- Comment
- Accepted by the 2023 International Conference on Information Systems (ICIS 2023)
- Language
- English
- Date posted
- 10/04/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984701729802771
Metrics
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