A Smart System For Fake News Detection Using Machine Learning

The rapid misinformation across social media and digital news platforms has intensified the global demand for automated fake news detection systems. This research paper presents a comprehensive study on detecting fake news using machine learning methods, following a structured experimental pipeline implemented in Python. Textual data sourced from publicly available true and fake news datasets were preprocessed, cleaned, normalized, and vectorized using TfidfVectorizer. Four supervised learning algorithms—Logistic Regression, Decision Tree Classifier, Gradient Boosting Classifier, and Random Forest Classifier—were implemented and evaluated. The study ultimately highlights both the promise and constraints of traditional machine learning models in addressing contemporary misinformation challenges.