Dual stream graph augmented transformer model integrating BERT and GNNs for context aware fake news detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel Dual-Stream Graph-Augmented Transformer Model for advanced fake news detection. By integrating textual analysis with graph-based propagation patterns, the model achieves superior accuracy in identifying misinformation online.
Area Of Science
- Computer Science
- Artificial Intelligence
- Network Science
Background
- Misinformation proliferation necessitates advanced detection methods.
- Traditional fake news detection often overlooks news propagation structures.
- Credibility assessment requires integrating linguistic and network features.
Purpose Of The Study
- To propose a Dual-Stream Graph-Augmented Transformer Model for enhanced fake news detection.
- To leverage both BERT for textual representation and Graph Neural Networks (GNNs) for propagation structure analysis.
- To improve fake news detection by combining linguistic and network-based features.
Main Methods
- Utilized BERT for deep textual understanding.
- Employed Graph Attention Networks (GAT) and Graph Transformers to model propagation structures.
- Integrated textual and graph embeddings using an attention-based fusion mechanism for classification.
Main Results
- Achieved 99% accuracy on the FakeNewsNet dataset.
- Outperformed baseline models like Bi-LSTM and RoBERTa-GCN.
- Demonstrated superior performance in fake news detection using combined features.
Conclusions
- Incorporating graph-based propagation features significantly enhances fake news detection.
- The proposed model offers a robust, scalable, and context-aware solution.
- Future work includes multilingual and multimodal misinformation detection.
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