Fake News Data Exploration and Analytics

Awan, Mazhar Javed, Yasin, Awais, Nobanee, Haitham ORCID: 0000-0003-4424-5600, Ali, Ahmed Abid, Shahzad, Zain, Nabeel, Muhammad, Zain, Azlan Mohd and Shahzad, Hafiz Muhammad Faisal
(2021) Fake News Data Exploration and Analytics. ELECTRONICS, 10 (19). p. 2326.

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<jats:p>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.</jats:p>

Item Type: Article
Uncontrolled Keywords: detection, fake news, data exploration, analytics, machine learning, random forest, logistic regression, big data, TF-IDF, natural language processing, unstructured data
Divisions: Faculty of Humanities and Social Sciences > School of Histories, Languages and Cultures
Depositing User: Symplectic Admin
Date Deposited: 21 Jan 2022 16:50
Last Modified: 15 Mar 2024 18:12
DOI: 10.3390/electronics10192326
Open Access URL: https://www.mdpi.com/2079-9292/10/19/2326
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3147363