Open AccessA Reliable Artificial Intelligence Model for False News Detection Made by Public Figures
Author(s) -
P. K. Shrivastava,
Mayank Sharma,
Megha Kamble,
Vaibhav Gore
Publication year - 2021
Publication title -
computer applications : an international journal
Language(s) - English
Resource type - Journals
ISSN - 2393-8455
DOI - 10.5121/caij.2021.8301
Subject(s) - credibility , computer science , social media , fake news , artificial neural network , identification (biology) , binary classification , artificial intelligence , information retrieval , data mining , data science , machine learning , internet privacy , world wide web , political science , support vector machine , botany , law , biology
The quick access to information on social media networks as well as its exponential rise also made it difficult to distinguish among fake information or real information. The fast dissemination by way of sharing has enhanced its falsification exponentially. It is also important for the credibility of social media networks to avoid the spread of fake information. So it is emerging research challenge to automatically check for misstatement of information through its source, content, or publisher and prevent the unauthenticated sources from spreading rumours. This paper demonstrates an artificial intelligence based approach for the identification of the false statements made by social network entities. Two variants of Deep neural networks are being applied to evalues datasets and analyse for fake news presence. The implementation setup produced maximum extent 99% classification accuracy, when dataset is tested for binary (true or false) labeling with multiple epochs.