Linguistic feature based learning model for fake news detection and classification

Social media is used as a dominant source of news distribution among users. The world's preeminent decisions such as politics are acclaimed by social media to influence users for enclosing users' decisions in their favor. However, the adoption of social media is much needed for awareness but the authenticity of content is an unknown factor in the current scenario. Therefore, this research work finds it imperative to propose a solution to fake news detection and classification. In the case of fake news, content is the prime entity that captures the human mind towards trust for specific news. Therefore, a linguistic model is proposed to find out the properties of content that will generate language-driven features. This linguistic model extracts syntactic, grammatical, sentimental, and readability features of particular news. Language driven model requires an approach to handle time-consuming and handcrafted features problems in order to deal with the curse of dimensionality problem. Therefore, the neural-based sequential learning model is used to achieve superior results for fake news detection. The results are drawn to validate the importance of the linguistic model extracted features and finally combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification. The sequential neural model results are compared with machine learning based models and LSTM based word embedding based fake news detection model as well. Comparative results show that features based sequential model is able to achieve comparable evaluation performance in discernable less time.