Open AccessReducing Fraudulent News Proliferation using Classification Techniques
Author(s) -
A. Raghuvira Pratap,
Jason Prasad,
Sallagundla Babu,
Vineet Kumar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6022.029320
Subject(s) - computer science , the internet , social media , classifier (uml) , world wide web , set (abstract data type) , reliability (semiconductor) , key (lock) , artificial intelligence , computer security , power (physics) , physics , quantum mechanics , programming language
The expansion of dishonorable information in normal get entry to social access media retailers like internet based media channels, news web journals, and online papers have made it hard to identify dependable news sources, subsequently growing the need for technique tools able to deliver insights into the reliability of online content substances.. This paper comes up with the applications of Natural language process techniques for detective work the dishonest news, that is, dishonorable news stories that return from the non-reputable sources. Solely by building a model supported mistreatment word tallies or a Term Frequency-Inverse Document Frequency matrix, will solely get you to date. Is it potential for you to make a model which will differentiate between “Real “news and “Fake” news? Thus our planned work is going to be on grouping a knowledge set of each pretend and real news and uses a Naïve mathematician classifier so as to make a model to classify an editorial into pretend or really supported its words and phrases.