Fake News Article classification using Random Forest, Passive Aggressive, and Gradient Boosting

Because of the exponential expansion of knowledge available on the internet, it is becoming impossible to decipher Real News from false News. Thus, this contributes to the spread of false information. Many dangerous fake accounts have been created recently, and these accounts distribute false information via posts, blogs, etc. across social media. Some people spread this false information without being aware of its falsity. In this proposal, we proposed a model to identify the fake news spreading on social media. To accomplish this model, we collected the dataset named “NEWS” from the Kaggle depository. Machine learning algorithms such as Random Forest, Passive Aggressive, and Gradient Boosting were used to Classify Real News and Fake News from News Articles. The passive Aggressive Algorithm gave better accuracy than the other two Algorithms used in this work.