An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way t...

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Autores principales:	Bader Alouffi, Abdullah Alharbi, Radhya Sahal, Hager Saleh
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/b40f26d1c7b6438da037050df7eb9943
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spelling	oai:doaj.org-article:b40f26d1c7b6438da037050df7eb99432021-11-29T00:56:11ZAn Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information1687-527310.1155/2021/9615034https://doaj.org/article/b40f26d1c7b6438da037050df7eb99432021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9615034https://doaj.org/toc/1687-5273Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.Bader AlouffiAbdullah AlharbiRadhya SahalHager SalehHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Bader Alouffi Abdullah Alharbi Radhya Sahal Hager Saleh An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
description	Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.
format	article
author	Bader Alouffi Abdullah Alharbi Radhya Sahal Hager Saleh
author_facet	Bader Alouffi Abdullah Alharbi Radhya Sahal Hager Saleh
author_sort	Bader Alouffi
title	An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
title_short	An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
title_full	An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
title_fullStr	An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
title_full_unstemmed	An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
title_sort	optimized hybrid deep learning model to detect covid-19 misleading information
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/b40f26d1c7b6438da037050df7eb9943
work_keys_str_mv	AT baderalouffi anoptimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT abdullahalharbi anoptimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT radhyasahal anoptimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT hagersaleh anoptimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT baderalouffi optimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT abdullahalharbi optimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT radhyasahal optimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation AT hagersaleh optimizedhybriddeeplearningmodeltodetectcovid19misleadinginformation
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An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information

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