Analyzing and Detecting the Fake News using Machine Learning

Fake news has grown to be a significant concern in the age of social media and online information sharing. False news detection is a difficult problem requiring a multidisciplinary approach combining natural language processing, machine learning, and social network research. This paper provides a short review of the area by classifying the research on false news identification into three categories: feature-based approaches, machine learning methods, and social network analysis methods. Further, we have applied machine learning algorithms to identify false news. We look at various feature types used for false news identification, including textual features, metadata features, and social context features. Our proposed system performed well with a classification accuracy of 99.70%. Furthermore, we delineate prospective remedies for the issues and constraints of existing fake news detection methods.