AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

Abstract

Social networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$</tex-math> </inline-formula> score of 98.33%, which outperforms other existing methods.