Fake News Detection Using Natural Language Processing And Machine Learning

The rapid spread of misinformation on digital platforms has created a pressing need for effective automated solutions. This project presents a fake news detection framework using Natural Language Processing (NLP) and Machine Learning (ML) techniques. Using a labeled dataset of real and fake news articles, the study applies TF-IDF-based feature extraction and evaluates four classification algorithms—Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The Random Forest model, after hyperparameter tuning, achieved the highest accuracy of 99.8% and an F1-score of 0.9979. The research also explores challenges like domain adaptation and model explainability, emphasizing the importance of ethical AI in combating misinformation. Results demonstrate that even lightweight ML models can be highly effective for fake news detection, offering scalable solutions for content moderation and public information integrity.