Fake News Detection System Using Machine Learning and Natural Language Processing

The rapid growth of social media and digital platforms has made it extremely easy for false information, commonly known as fake news, to spread at a massive scale. This misinformation causes serious damage to society, affecting political decisions, public health responses, and general trust in news sources. Traditional methods of manually fact-checking news are slow and cannot keep up with the huge volume of content being published every day. This paper presents a Fake News Detection System that uses Machine Learning (ML) and Natural Language Processing (NLP) techniques to automatically identify whether a given news article is real or fake. The system uses TF-IDF (Term Frequency-Inverse Document Frequency) for converting text into numerical features, and applies two classification algorithms — Logistic Regression and Naive Bayes — to predict the authenticity of news. The model is trained and tested on publicly available datasets. Experimental results show that Logistic Regression achieved an accuracy of approximately 93%, while Naive Bayes achieved around 89%. The proposed system provides a fast, scalable, and reasonably accurate approach to tackling the growing problem of fake news in the digital age.