Fake News Detection using NLP

Fake news has become a major challenge in the digital era due to the rapid growth of social media and online news platforms. The widespread dissemination of misleading information can influence public opinion, political decisions, and social stability. Traditional fact-checking approaches are time-consuming and incapable of handling the massive volume of online content generated daily. This research proposes an automated Fake News Detection system using Natural Language Processing (NLP) and transformer-based deep learning techniques. The proposed methodology utilizes text preprocessing, tokenization, stop-word removal, lemmatization, and contextual feature extraction through Bidirectional Encoder Representations from Transformers (BERT). The extracted features are classified using a deep learning classifier to determine whether a news article is real or fake. Experimental evaluation performed on publicly available fake news datasets demonstrates high classification accuracy, precision, recall, and F1-score. The proposed system provides a scalable and efficient solution for combating misinformation and improving the reliability of digital information.