Fake News Detection using Natural Language Processing (NLP)

The rapid growth of online information, especially through social media, has made it difficult to differentiate between real and fake news.The widespread sharing of inaccurate or misleading content poses a serious threat to public trust, social stability, and informed decision-making.This study focuses on detecting fake news using Natural Language Processing (NLP) and machine learning techniques.The research includes text preprocessing, feature extraction using TF-IDF and word embeddings, followed by classification using algorithms such as Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, and XGBoost.Among these, XGBoost achieved the highest accuracy, followed by Decision Tree and Random Forest, demonstrating its effectiveness in fake news classification.The study also reviews existing literature, highlights the limitations of earlier LSTM-based approaches, and emphasizes the importance of NLP models like BERT that better understand semantic context.Results indicate that machine learning combined with NLP significantly improves fake news detection accuracy.This work concludes that automated fake news detection systems can help reduce misinformation and enhance digital media reliability.Future advancements may include multilingual support, hybrid models, and real-time deployment on social media platforms.