An NLP-Based Approach for Fake News Classification Using Supervised Machine Learning Algorithms

Abstract

The rapid spread of misinformation through social media and online news platforms has made fake news detection an important research problem. This project proposes a Natural Language Processing (NLP) based approach to classify news articles as real or fake using supervised machine learning algorithms. The system performs text preprocessing and feature extraction using techniques such as TF-IDF to convert textual data into numerical representations. Several classification models including Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree are used to train and evaluate the dataset. The performance of the models is measured using accuracy, precision, recall, and F1-score to ensure reliable prediction. The proposed system helps users identify misleading information and supports the prevention of misinformation spread in digital media.