Staff View: Fake news data exploration and analytics

Fake news data exploration and analytics

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one...

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Main Authors:	Awan, Mazhar Javed, Yasin, Awais, Nobanee, Haitham, Ali, Ahmed Abid, Shahzad, Zain, Nabeel, Muhammad, Mohd. Zain, Azlan, Shahzad, Hafiz Muhammad Faisal
Format:	Article
Language:	English
Published:	MDPI 2021
Subjects:	QA75 Electronic computers. Computer science T58.5-58.64 Information technology
Online Access:	http://eprints.utm.my/id/eprint/94492/1/AzlanMohdZain2021_FakeNewsDataExplorationandAnalytics.pdf http://eprints.utm.my/id/eprint/94492/ http://dx.doi.org/10.3390/electronics10192326
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id	my.utm.94492
record_format	eprints
spelling	my.utm.944922022-03-31T15:46:37Z http://eprints.utm.my/id/eprint/94492/ Fake news data exploration and analytics Awan, Mazhar Javed Yasin, Awais Nobanee, Haitham Ali, Ahmed Abid Shahzad, Zain Nabeel, Muhammad Mohd. Zain, Azlan Shahzad, Hafiz Muhammad Faisal QA75 Electronic computers. Computer science T58.5-58.64 Information technology Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications. MDPI 2021-09-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94492/1/AzlanMohdZain2021_FakeNewsDataExplorationandAnalytics.pdf Awan, Mazhar Javed and Yasin, Awais and Nobanee, Haitham and Ali, Ahmed Abid and Shahzad, Zain and Nabeel, Muhammad and Mohd. Zain, Azlan and Shahzad, Hafiz Muhammad Faisal (2021) Fake news data exploration and analytics. Electronics (Switzerland), 10 (19). pp. 1-15. ISSN 2079-9292 http://dx.doi.org/10.3390/electronics10192326 DOI:10.3390/electronics10192326
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science T58.5-58.64 Information technology
spellingShingle	QA75 Electronic computers. Computer science T58.5-58.64 Information technology Awan, Mazhar Javed Yasin, Awais Nobanee, Haitham Ali, Ahmed Abid Shahzad, Zain Nabeel, Muhammad Mohd. Zain, Azlan Shahzad, Hafiz Muhammad Faisal Fake news data exploration and analytics
description	Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.
format	Article
author	Awan, Mazhar Javed Yasin, Awais Nobanee, Haitham Ali, Ahmed Abid Shahzad, Zain Nabeel, Muhammad Mohd. Zain, Azlan Shahzad, Hafiz Muhammad Faisal
author_facet	Awan, Mazhar Javed Yasin, Awais Nobanee, Haitham Ali, Ahmed Abid Shahzad, Zain Nabeel, Muhammad Mohd. Zain, Azlan Shahzad, Hafiz Muhammad Faisal
author_sort	Awan, Mazhar Javed
title	Fake news data exploration and analytics
title_short	Fake news data exploration and analytics
title_full	Fake news data exploration and analytics
title_fullStr	Fake news data exploration and analytics
title_full_unstemmed	Fake news data exploration and analytics
title_sort	fake news data exploration and analytics
publisher	MDPI
publishDate	2021
url	http://eprints.utm.my/id/eprint/94492/1/AzlanMohdZain2021_FakeNewsDataExplorationandAnalytics.pdf http://eprints.utm.my/id/eprint/94492/ http://dx.doi.org/10.3390/electronics10192326
_version_	1729703179820466176
score	13.211869

Fake news data exploration and analytics

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