BERT-Based Fake News Detection: A Transformer-Driven Approach for Misinformation Classification on Twitter

Roise Uddin; Abdul Basit; Yearanoor Khan; MD Sahria Jaman Shazib; Shahadat Hossain

doi:10.71097/IJSAT.v16.i1.2023

BERT-Based Fake News Detection: A Transformer-Driven Approach for Misinformation Classification on Twitter

Author(s)	Roise Uddin, Abdul Basit, Yearanoor Khan, MD Sahria Jaman Shazib, Shahadat Hossain
Country	India
Abstract	The rapid spread of fake news on social media platforms, particularly Twitter, poses a critical challenge to information credibility. This research presents an advanced fake news detection framework leveraging deep learning models, including XGBoost, CNN-RNN, BERT, and RoBERTa + GNN, to enhance detection accuracy. Our approach integrates content-based analysis, social context features, and explainable AI techniques (SHAP, LIME) for robust classification.We trained and evaluated our models on the FakeNewsNet, PolitiFact, and Kaggle fake news datasets, employing state-of-the-art feature engineering techniques such as semantic embeddings (RoBERTa, XLNet), sentiment analysis, and network propagation modeling. Experimental results demonstrate that our RoBERTa + GNN model achieves the highest accuracy of 98.7%, outperforming BERT (98.0%), CNN-RNN (84.0%), and XGBoost (81.0%). The precision, recall, and F1-scores of our models also indicate strong classification performance, with RoBERTa + GNN achieving an F1-score of 98.4%.By integrating explainability techniques, we ensure model transparency, allowing insights into the key linguistic and contextual factors influencing classification. This research contributes to improving automated misinformation detection, reducing the impact of fake news, and supporting real-time deployment for social media monitoring. Future work includes expanding cross-lingual capabilities and enhancing early detection using temporal features.
Keywords	Fake News Detection, Deep Learning, Transformer Models, BERT, RoBERTa, Graph Neural Networks (GNN), Natural Language Processing (NLP), Misinformation.
Field	Computer Applications
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-02-24
Cite This	BERT-Based Fake News Detection: A Transformer-Driven Approach for Misinformation Classification on Twitter - Roise Uddin, Abdul Basit, Yearanoor Khan, MD Sahria Jaman Shazib, Shahadat Hossain - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2023
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2023
Short DOI	https://doi.org/g8595h

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BERT-Based Fake News Detection: A Transformer-Driven Approach for Misinformation Classification on Twitter

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