Weiqin Tong
Biographic Data
| ID | 10054226 |
|---|---|
| NAME | Weiqin Tong |
| GIVEN NAMES | Weiqin |
| FAMILY NAME | Tong |
| SIGNATURE | TONG W |
| AFFILIATIONS | Shanghai University of Engineering Science |
| ORCID | 0000-0001-8300-6376 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Rumor Detection Based on Supervised Multiprototype Contrastive Learning
Graph neural networks (GNNs) have shown great promise in rumor detection by leveraging user interactions and propagation structures. However, existing GNN-based methods primarily aggregate low-frequency signals, leading to oversmoothing and the loss of distinctive features in user feedback. Additionally, the data imbalance and sparsity in social media platforms hinder the training of robust detection models. To address these challenges, this arti…
Graph Contrastive Learning With Feature Augmentation for Rumor Detection
While online social media brings convenience to people’s communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt to identify rumors by engaging in interactive user feedback. However, the performance of these models suffers from insufficient and noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmen…
No prominent works on this page.
Graph Contrastive Learning With Feature Augmentation for Rumor Detection
While online social media brings convenience to people’s communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt to identify rumors by engaging in interactive user feedback. However, the performance of these models suffers from insufficient and noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmen…
Rumor Detection Based on Supervised Multiprototype Contrastive Learning
Graph neural networks (GNNs) have shown great promise in rumor detection by leveraging user interactions and propagation structures. However, existing GNN-based methods primarily aggregate low-frequency signals, leading to oversmoothing and the loss of distinctive features in user feedback. Additionally, the data imbalance and sparsity in social media platforms hinder the training of robust detection models. To address these challenges, this arti…
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Misinformation and Its Impacts (2 works) · Natural language processing (2 works) · Rumor (2 works) · Advanced Text Analysis Techniques (1 work) · Complex Network Analysis Techniques (1 work) · Computer security (1 work) · Feature learning (1 work)