Identification of Fake News Using Machine Learning Approach

Abstract

In today’s world everyone is using internet and Social media platforms like Instagram, Twitter, Facebook etc. In such scenario fake news spreads very rapidly and reaches to millions of user in a short span of time. Riots during election, riots between different religious groups are consequences of these fake news. Many political parties utilize false information to boost their vote totals. Machine learning is important for classification of data, but it has limitations. On the basis of the kaggle dataset, a model has been suggested to classify fake and authentic news in this project. Our technology will be programmed to discern between fake and legitimate news from various social media platforms. It also aims to distinguish genuine news from a variety of sources. Our research looks into several textual qualities that can be used to tell the difference between phoney and real content. We use machine learning algorithms, such as the Passive Aggressive classifier using TF-IDF, to train our model utilizing those properties.