Dual stream graph augmented transformer model integrating BERT and GNNs for context aware fake news detection.

Journal: Scientific reports
Published Date: Jul 14, 2025

Abstract

The rapid proliferation of misinformation across digital platforms has highlighted the critical need for advanced fake news detection mechanisms. Traditional methods primarily rely on textual analysis, often neglecting the structural patterns of news dissemination, which play a crucial role in determining credibility. To address this limitation, this study proposes a Dual-Stream Graph-Augmented Transformer Model, integrating BERT for deep textual representation and Graph Neural Networks (GNNs) to model the propagation structure of misinformation. The objective is to enhance fake news detection by leveraging both linguistic and network-based features. The proposed method employs Graph Attention Networks (GAT) and Graph Transformers to extract contextual relationships, while an attention-based fusion mechanism effectively integrates textual and graph embeddings for classification. The model is implemented using PyTorch and Hugging Face Transformers, with experiments conducted on the FakeNewsNet dataset, which includes news articles, user interactions, and source metadata. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC indicate superior performance, with an accuracy of 99%, outperforming baseline models such as Bi-LSTM and RoBERTa-GCN. The study concludes that incorporating graph-based propagation features significantly improves fake news detection, providing a robust, scalable, and context-aware solution. Future enhancements will focus on refining credibility assessment mechanisms and extending the model to support multilingual and multimodal misinformation detection across diverse digital platforms.

Authors

  • Hejamadi Rama Moorthy
    Nitte (Deemed to be University), Nitte Institute of Professional Education (NIPE), Department of Computer Applications, Mangalore, Karnataka, India.
  • N J Avinash
    Department of Electronics & Communication Engineering, Mangalore Institute of Technology and Engineering, Moodabidre, Karnataka, India.
  • N S Krishnaraj Rao
    Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Department of Information Science and Engineering, Nitte, Karnataka, India. krishnaraj.rao@nitte.edu.in.
  • K R Raghunandan
    Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Department of Computer Science and Engineering, Nitte, Karnataka, India. raghunandan@nitte.edu.in.
  • Radhakrishna Dodmane
    Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Department of Computer & Communication Engineering, Nitte, Karnataka, India.
  • Jeremy Joseph Blum
    Department of Computer Science and Mathematics, Pennsylvania State University, Harrisburg, USA.
  • Lubna A Gabralla
    Department of Computer Science, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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