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Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm

1
Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
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School of Cybersecurity, Data Science and Computing, Norwich University, Northfield, VT 05663, USA
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Mininglamp Academy of Sciences, Mininglamp Technology, Beijing 100864, China
*
Author to whom correspondence should be addressed.
Academic Editor: Giancarlo Mauri
Appl. Sci. 2021, 11(16), 7265; https://doi.org/10.3390/app11167265
Received: 17 June 2021 / Revised: 24 July 2021 / Accepted: 30 July 2021 / Published: 6 August 2021
(This article belongs to the Section Computing and Artificial Intelligence)
The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm was trained by Term Frequency Inverse Document Frequency (TF-IDF) bigram features, which contribute a network training model. The algorithm was tested on two different real-world datasets from the CBC news network and COVID-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97–99%. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents, which may contribute negatively to the COVID-19 pandemic. View Full-Text
Keywords: COVID-19 infodemic; text classification; TF-IDF features; network training modes; supervised learning; misinformation; news classification; false publications; PubMed anomaly detection COVID-19 infodemic; text classification; TF-IDF features; network training modes; supervised learning; misinformation; news classification; false publications; PubMed anomaly detection
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MDPI and ACS Style

Abdeen, M.A.R.; Hamed, A.A.; Wu, X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Appl. Sci. 2021, 11, 7265. https://doi.org/10.3390/app11167265

AMA Style

Abdeen MAR, Hamed AA, Wu X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Applied Sciences. 2021; 11(16):7265. https://doi.org/10.3390/app11167265

Chicago/Turabian Style

Abdeen, Mohammad A.R., Ahmed A. Hamed, and Xindong Wu. 2021. "Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm" Applied Sciences 11, no. 16: 7265. https://doi.org/10.3390/app11167265

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