Tapering Malicious Language for Identifying Fake Web Content

Abstract

The neoteric occurrence, the pandemic, and global crisis entails the extensive use of web portals to unfurl information. While this has built the cognizance of the common man, the infinitely unnoticed enumeration of malicious content on the web has escalated copiously. Spurious data and fake information has done more harm than what is actually unraveled to the public; however, scrupulously meticulous measures to agonize their source and delve into mitigating these data has become quite a challenge. This indignation delves into step-wise analysis of identifying the hoax through systematically programmed algorithms using natural language processing.