A MACHINE LEARNING FRAMEWORK FOR DETECTING FALSE NEWS USING EMBEDDINGS AND DIMENSIONALITY REDUCTION

widespread dissemination of knowledge in human history was made possible by the creation of the World Wide Web and the rapid adoption of social media platforms like Facebook and Twitter.Because social media is so widely used, consumers are creating and sharing more information than ever before, some of it is false and unconnected to reality. Automatically classifying a written article as misinformation or disinformation can be challenging. Even a subject-matter expert must take several aspects into account before determining the authenticity of an article. In this work, we propose a machine learning approach for automatically classifying news articles. Our study looks into a variety of linguistic traits that can be used to distinguish between genuine and fake content. Using those qualities, we train a range of machine learning techniques and deep learning models. Using the Long Short term memory (LSTM) model, we eventually achieved 97% accuracy.