Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analys...

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Autores principales:	Waqas Haider Bangyal, Rukhma Qasim, Najeeb ur Rehman, Zeeshan Ahmad, Hafsa Dar, Laiqa Rukhsar, Zahra Aman, Jamil Ahmad
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/9239b27f16d54fe29693fee0fff100b7
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spelling	oai:doaj.org-article:9239b27f16d54fe29693fee0fff100b72021-11-29T00:56:00ZDetection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches1748-671810.1155/2021/5514220https://doaj.org/article/9239b27f16d54fe29693fee0fff100b72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5514220https://doaj.org/toc/1748-6718A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.Waqas Haider BangyalRukhma QasimNajeeb ur RehmanZeeshan AhmadHafsa DarLaiqa RukhsarZahra AmanJamil AhmadHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Waqas Haider Bangyal Rukhma Qasim Najeeb ur Rehman Zeeshan Ahmad Hafsa Dar Laiqa Rukhsar Zahra Aman Jamil Ahmad Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
description	A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.
format	article
author	Waqas Haider Bangyal Rukhma Qasim Najeeb ur Rehman Zeeshan Ahmad Hafsa Dar Laiqa Rukhsar Zahra Aman Jamil Ahmad
author_facet	Waqas Haider Bangyal Rukhma Qasim Najeeb ur Rehman Zeeshan Ahmad Hafsa Dar Laiqa Rukhsar Zahra Aman Jamil Ahmad
author_sort	Waqas Haider Bangyal
title	Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
title_short	Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
title_full	Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
title_fullStr	Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
title_full_unstemmed	Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
title_sort	detection of fake news text classification on covid-19 using deep learning approaches
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/9239b27f16d54fe29693fee0fff100b7
work_keys_str_mv	AT waqashaiderbangyal detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT rukhmaqasim detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT najeeburrehman detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT zeeshanahmad detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT hafsadar detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT laiqarukhsar detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT zahraaman detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches AT jamilahmad detectionoffakenewstextclassificationoncovid19usingdeeplearningapproaches
_version_	1718407720595357696

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

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