Open AccessFake News Detection
Author(s) -
Lakesh Jat,
Mansi Mohite,
Radhika Choudhari,
Pooja Shelke
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1347
Subject(s) - fake news , credibility , computer science , set (abstract data type) , social media , information retrieval , data science , world wide web , internet privacy , political science , law , programming language
This research paper aims to examine the principles, procedures and algorithms for finding fake news, creators and topics on online social networks and to evaluate the relevant performance. This paper deals with the unknown features of fake news and the challenges identified by the various relationships between news articles, creators and subjects. This paper introduces a novel automated fake news credibility estimation model called “fake news detection”. Based on a set of explicit and latent features extracted from text information, “Fake News Detection” builds an in-depth network model to simultaneously learn news articles, producers, and topic presentations. Extensive experiments have been carried out on real-world fake news datasets to compare "fake news detection" with many "sophisticated models", and experimental results have shown the effectiveness of the proposed model.