The Detection and Analysis of Fake News Using Machine Learning

Abstract

Social media has become one of the hotbeds for the dissemination of fake news in the market. Although, the role of social media is very important for the campaign, broadcaster of any news, and trend formulation. However, it may be the reason for the dissemination of fake news and bring negative impactual results on society and individuals. Even some significant impacts on election campaigns, politics, trend settings, and marketing agencies can be executed using social media. So the detection of fake news is one of the most perpetual ways to set the right trend in the market. Traditional methods with manual filtering are not feasible for the detection of fake news effectively. Although, there are several techniques used to detect fake news such as data mining, natural language processing, social network analysis, and machine learning algorithms. In this paper, the focus is to detect false news using distinctive machine learning algorithms. The model is trained and tested of the data-set available freely as an open-source. The positive prediction rate is very high in the ROC curve indicates the prediction of fake news effectively. Our experiment indicates the high accuracy with the support vector machine classifier algorithm.