Fake News Detection Using Deep Learning a Transformer Model Approach

The rapid spread of fake news has become a critical challenge in the digital era, influencing public perception, political decisions, and societal discourse. Traditional fake news detection approaches often struggle to capture the complex linguistic patterns and contextual dependencies in deceptive content. To address these limitations, this study leverages transformer-based deep learning models, such as BERT and its variants, to enhance fake news detection accuracy. The proposed methodology incorporates an advanced preprocessing pipeline, including BERTspecific tokenization, to improve contextual understanding and mitigate data inconsistencies. Furthermore, the model's learning process is analysed through training-validation loss and accuracy plots, ensuring optimized performance. Experimental results demonstrate that the transformer-based approach significantly outperforms conventional deep learning techniques in distinguishing real from fake news. The study highlights the advantages of contextual embeddings in capturing subtle textual nuances and provides insights into the practical implementation of deep learning for misinformation detection. This research contributes to the growing field of automated fake news detection by offering a scalable, interpretable, and robust solution for identifying deceptive online content.