Fake News Detection Using Machine Learning and Natural Language Processing

The study addresses the challenge of fake news in the digital era by employing a variety of machine-learning techniques, including logistic regression, decision trees, gradient boosting, LSTM, and BERT models. It uses a dataset from Kaggle consisting of news articles from different sources to develop methods for distinguishing between genuine and fabricated information. Through natural language processing (NLP), linguistic features such as textual patterns and sentiment analysis are extracted from the news content. These features are then utilized to train and evaluate the performance of various machine-learning models in identifying fake news. By combining traditional statistical methods with advanced deep learning architectures, the research aims to gain a comprehensive understanding of fake news characteristics and create robust detection mechanisms. The study emphasizes the importance of adopting a multi-dimensional approach to combat misinformation online, highlighting the necessity for a comprehensive strategy in addressing the proliferation of fake news on popular online platforms.