A Novel Approach to Detection of Fake News in Online Communities

Abstract

Fake news serving various political and commercial agendas has emerged on the web and spread rapidly in recent years, thanks in large part to the proliferation of online social networks. People who use informal online groups are especially vulnerable to the sneaky effects of deceptive language used in fake news on the internet, which has far-reaching effects on real society. To make information in informal online communities more reliable, it is important to be able to spot fake news as soon as possible. The goal of this study is to look at the criteria, methods, and calculations that are used to find and evaluate fake news, content, and topics in unstructured online communities. This research is mostly about how vague fake news is and how many connections there are between articles, writers, and topics. In this piece, we introduce FAKEDETECTOR, a novel controlled graph neural network. FAKEDETECTOR creates a deep diffusive organization model based on a wide range of explicit and specific attributes extracted from the textual content, allowing it to simultaneously learn the models of reports, authors, and topics. The complete version of this paper provides exploratory results from extensive experiments on a real fake news dataset designed to distinguish FAKEDETECTOR from two state-of-the-art algorithms.