Fake News Detection Using Machine Learning and Natural Language Processing

The spread of fake news on social media and online platforms has become a serious problem in today’s digital world. Fake news can mislead people, create panic, and even influence political or social opinions. Traditional methods of manual fact-checking are too slow and inefficient, especially given the huge volume of online content. This research focuses on the use of Machine Learning (ML) and Natural Language Processing (NLP) techniques to automatically detect fake news. Different algorithms such as Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, and deep learning models like Long Short-Term Memory (LSTM) are explored. The paper also discusses feature extraction methods such as TF-IDF, Bag-of-Words, and Word Embedding’s. The expected outcome of this research is to design an accurate, efficient, and real-time fake news detection system that can be applied to social media monitoring, news websites and digital media platforms.