A Comprehensive Review on Fake News Detection With Deep Learning

A protuberant issue of the present time is that, organizations from different domains are struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to distinguish fake information on the internet as it is often written to deceive users. Compared wit...

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Autores principales:	M. F. Mridha, Ashfia Jannat Keya, Md. Abdul Hamid, Muhammad Mostafa Monowar, Md. Saifur Rahman
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Natural language processing machine learning deep learning fake news Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f00aedd9576e4124ad7738eed76c9318
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spelling	oai:doaj.org-article:f00aedd9576e4124ad7738eed76c93182021-12-01T00:01:08ZA Comprehensive Review on Fake News Detection With Deep Learning2169-353610.1109/ACCESS.2021.3129329https://doaj.org/article/f00aedd9576e4124ad7738eed76c93182021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9620068/https://doaj.org/toc/2169-3536A protuberant issue of the present time is that, organizations from different domains are struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to distinguish fake information on the internet as it is often written to deceive users. Compared with many machine learning techniques, deep learning-based techniques are capable of detecting fake news more accurately. Previous review papers were based on data mining and machine learning techniques, scarcely exploring the deep learning techniques for fake news detection. However, emerging deep learning-based approaches such as Attention, Generative Adversarial Networks, and Bidirectional Encoder Representations for Transformers are absent from previous surveys. This study attempts to investigate advanced and state-of-the-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. Then, we proceed with the discussion on the dataset used in previous research and their NLP techniques. A comprehensive overview of deep learning-based techniques has been bestowed to organize representative methods into various categories. The prominent evaluation metrics in fake news detection are also discussed. Nevertheless, we suggest further recommendations to improve fake news detection mechanisms in future research directions.M. F. MridhaAshfia Jannat KeyaMd. Abdul HamidMuhammad Mostafa MonowarMd. Saifur RahmanIEEEarticleNatural language processingmachine learningdeep learningfake newsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156151-156170 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Natural language processing machine learning deep learning fake news Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Natural language processing machine learning deep learning fake news Electrical engineering. Electronics. Nuclear engineering TK1-9971 M. F. Mridha Ashfia Jannat Keya Md. Abdul Hamid Muhammad Mostafa Monowar Md. Saifur Rahman A Comprehensive Review on Fake News Detection With Deep Learning
description	A protuberant issue of the present time is that, organizations from different domains are struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to distinguish fake information on the internet as it is often written to deceive users. Compared with many machine learning techniques, deep learning-based techniques are capable of detecting fake news more accurately. Previous review papers were based on data mining and machine learning techniques, scarcely exploring the deep learning techniques for fake news detection. However, emerging deep learning-based approaches such as Attention, Generative Adversarial Networks, and Bidirectional Encoder Representations for Transformers are absent from previous surveys. This study attempts to investigate advanced and state-of-the-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. Then, we proceed with the discussion on the dataset used in previous research and their NLP techniques. A comprehensive overview of deep learning-based techniques has been bestowed to organize representative methods into various categories. The prominent evaluation metrics in fake news detection are also discussed. Nevertheless, we suggest further recommendations to improve fake news detection mechanisms in future research directions.
format	article
author	M. F. Mridha Ashfia Jannat Keya Md. Abdul Hamid Muhammad Mostafa Monowar Md. Saifur Rahman
author_facet	M. F. Mridha Ashfia Jannat Keya Md. Abdul Hamid Muhammad Mostafa Monowar Md. Saifur Rahman
author_sort	M. F. Mridha
title	A Comprehensive Review on Fake News Detection With Deep Learning
title_short	A Comprehensive Review on Fake News Detection With Deep Learning
title_full	A Comprehensive Review on Fake News Detection With Deep Learning
title_fullStr	A Comprehensive Review on Fake News Detection With Deep Learning
title_full_unstemmed	A Comprehensive Review on Fake News Detection With Deep Learning
title_sort	comprehensive review on fake news detection with deep learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f00aedd9576e4124ad7738eed76c9318
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A Comprehensive Review on Fake News Detection With Deep Learning

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