Detection of Fake News using Machine Learning

Abstract

Regulation of false news causes challenges in the dissemination of accurate information and leads to misconceptions that endanger national cohesion and individual tranquility. Because information fabricates how the folk encapsulates the world, it is prime to combat this unauthentic news. People establish their own ideas in addition to basing critical judgments on these news articles, thus inaccurate news can exert a disastrous impact on the culture of a society making segmentation of publication pieces as spurious or authentic highly important. Numerous academicians are endeavoring to spot spurious news, and Machine Learning has been shown to be fruitful. In this paper, varied Machine Learning Algorithms are utilized to generate models to classify a particular news piece as authentic or fake. Python was applied as the scripting language throughout this development. Individual Machine Learning Algorithms such as K Nearest Neighbors and Decision Trees along with in-built ensembled classifiers (Random Forest, Gradient Boosting) and custom ensembled models (Stacking, Maximum Voting Classifier) are pattered for the purpose. With the avail of the appropriate models and tools, the challenging work of detecting false news may be made simple. This paper has been able to achieve an accuracy of 91.5% in classifying news as true or fake by stacking three individual Machine Learning Models namely, K Nearest Neighbors, Support Vector Classifier and Logistic Regression into a custom-ensembled model.