Optimization and improvement of fake news detection using deep learning approaches for societal benefit

Fake news is a topic that has been discussed for quite some time. Prior to the internet era, it was mostly distributed through yellow journalism, with a focus on sensational news such as crime, rumours, accidents, and amusing news. To rescue the life of people from these fake news propagation, detec...

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Autores principales:	Tavishee Chauhan, M.E, Hemant Palivela, PhD
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/e42684fa1752492b855e636c44a6d3d5
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spelling	oai:doaj.org-article:e42684fa1752492b855e636c44a6d3d52021-12-02T05:04:35ZOptimization and improvement of fake news detection using deep learning approaches for societal benefit2667-096810.1016/j.jjimei.2021.100051https://doaj.org/article/e42684fa1752492b855e636c44a6d3d52021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2667096821000446https://doaj.org/toc/2667-0968Fake news is a topic that has been discussed for quite some time. Prior to the internet era, it was mostly distributed through yellow journalism, with a focus on sensational news such as crime, rumours, accidents, and amusing news. To rescue the life of people from these fake news propagation, detection of fake news at an early stage becomes the most crucial step. People unknowingly propagate fake news and become a part of fake news propagation. While the original fake news propagators are the one with their aim to target innocent people for spreading the fake news. To stop this series of events, fake news detection and its pattern of propagation becomes very essential to society and the government. Various techniques exist to detect fake news in social media, among which neural networks have shown effective results. For this research, a deep learning based approach has been used to differentiate false news from the original ones. A LSTM neural network has been used to build the proposed model. Besides the neural network, a gloVe word embedding has been used for vector representation of textual words. Also, for feature extraction or vectorization, tokenization technique has been used. N-grams concept is used to enhance the proposed model. The comparative analysis of multiple fake news detection techniques is analysed. The results of proposed model have been evaluated using accuracy metrics. The model outperformed by achieving 99.88% of accuracy.Tavishee Chauhan, M.EHemant Palivela, PhDElsevierarticleInformation technologyT58.5-58.64ENInternational Journal of Information Management Data Insights, Vol 1, Iss 2, Pp 100051- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Information technology T58.5-58.64
spellingShingle	Information technology T58.5-58.64 Tavishee Chauhan, M.E Hemant Palivela, PhD Optimization and improvement of fake news detection using deep learning approaches for societal benefit
description	Fake news is a topic that has been discussed for quite some time. Prior to the internet era, it was mostly distributed through yellow journalism, with a focus on sensational news such as crime, rumours, accidents, and amusing news. To rescue the life of people from these fake news propagation, detection of fake news at an early stage becomes the most crucial step. People unknowingly propagate fake news and become a part of fake news propagation. While the original fake news propagators are the one with their aim to target innocent people for spreading the fake news. To stop this series of events, fake news detection and its pattern of propagation becomes very essential to society and the government. Various techniques exist to detect fake news in social media, among which neural networks have shown effective results. For this research, a deep learning based approach has been used to differentiate false news from the original ones. A LSTM neural network has been used to build the proposed model. Besides the neural network, a gloVe word embedding has been used for vector representation of textual words. Also, for feature extraction or vectorization, tokenization technique has been used. N-grams concept is used to enhance the proposed model. The comparative analysis of multiple fake news detection techniques is analysed. The results of proposed model have been evaluated using accuracy metrics. The model outperformed by achieving 99.88% of accuracy.
format	article
author	Tavishee Chauhan, M.E Hemant Palivela, PhD
author_facet	Tavishee Chauhan, M.E Hemant Palivela, PhD
author_sort	Tavishee Chauhan, M.E
title	Optimization and improvement of fake news detection using deep learning approaches for societal benefit
title_short	Optimization and improvement of fake news detection using deep learning approaches for societal benefit
title_full	Optimization and improvement of fake news detection using deep learning approaches for societal benefit
title_fullStr	Optimization and improvement of fake news detection using deep learning approaches for societal benefit
title_full_unstemmed	Optimization and improvement of fake news detection using deep learning approaches for societal benefit
title_sort	optimization and improvement of fake news detection using deep learning approaches for societal benefit
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e42684fa1752492b855e636c44a6d3d5
work_keys_str_mv	AT tavisheechauhanme optimizationandimprovementoffakenewsdetectionusingdeeplearningapproachesforsocietalbenefit AT hemantpalivelaphd optimizationandimprovementoffakenewsdetectionusingdeeplearningapproachesforsocietalbenefit
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Optimization and improvement of fake news detection using deep learning approaches for societal benefit

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