FAKE NEWS DETECTION USING MACHINE LEARNING

Abstract

Abstract: The rapid growth of social media platforms has significantly increased the spread of misinformation and fake news, posing serious societal challenges. Fake news can influence public opinion, disrupt democratic processes, and create social unrest. This research paper explores the application of machine learning techniques for detecting fake news. Various models such as Logistic Regression, Naïve Bayes, Support Vector Machines (SVM), and deep learning approaches like Long Short-Term Memory (LSTM) are analyzed. The study includes dataset preprocessing, feature extraction using TF-IDF, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results demonstrate that machine learning models can effectively classify fake and real news with high accuracy, with deep learning models showing superior performance. Keywords: Fake News, Machine Learning, Natural Language Processing, Classification, TF-IDF, Deep Learning