The impact on society of false news spreading on social media with the help of predictive modelling

Nowadays, the interaction on social media for the latest news is an excellent source of information. Most of the time we read online news that may primarily appear authentic, but we cannot assure it because it does not happen every time. According to Gartner's published report, by 2022, most mature economies will get fake information than the correct information, mainly through social media. Fake news is one of the prevalent threats in our digitally linked world. This paper proposes a model for recognising fake news through the dataset from the Kaggle. There was 3,000 news collected from various social media sources in the dataset, of which 2,725 news is a training dataset and 275 for the test dataset. The fake and real news is classified and compared using five machine learning classification algorithms and analysed accordingly. The five classification algorithms are support vector machine (SVM), naive Bayes, logistic regression, random forest, and neural networks.