FNDS: An Insight Into Fake News Detection System Using Machine Learning

With the rise of digital platforms, accessing news has become easier, but so has the circulation of misleading information, which affects public opinion and trust. Traditional manual fact-checking methods are unable to cope with the vast volume of online data. This paper introduces a machine learning powered solution for detecting fake news by utilizing Natural Language Processing (NLP) techniques. Specifically, the system employs Term Frequency Inverse Document Frequency (TF-IDF) strategy to retrieve meaningful textual features, which are then analyzed using a Logistic Regression model. A user-friendly web interface allows users to check the authenticity of articles, while an administrative review adds an extra layer of verification. This combined approach ensures improved accuracy and reliability in identifying fake news content.