Fake News Detection using BERT & ROBERTA

The fake news detection system leverages advanced transformer-based architectures—BERT and RoBERTa—to

accurately identify and classify misinformation in textual content. Unlike traditional NLP approaches, these pretrained language

models excel at capturing contextual nuances, semantics, and deeper linguistic patterns across long-range dependencies in text.

Fine-tuned on large-scale datasets containing both real and fake news articles, the system is capable of discerning subtle patterns

and inconsistencies often present in manipulated or misleading narratives. BERT’s bidirectional encoding and RoBERTa’s

optimized training strategies contribute to superior performance in understanding the complexity of natural language, ensuring

precise and reliable fake news detection. The backend of the system is built using Flask, providing efficient API endpoints that

allow users to input text data. Upon submission, the model evaluates the input and classifies it as either fake or real,

accompanied by a confidence score to reflect the likelihood of misinformation.To maintain robustness and adaptability, the

system supports continuous learning, allowing the models to be retrained with new data to keep pace with evolving deceptive

techniques in news dissemination. Model performance is evaluated using key metrics such as accuracy, precision, recall, and F1-

score, ensuring that the system remains both dependable and scalable for real-world applications. This makes the proposed

framework highly effective in combating the spread of fake news across digital platforms.