Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm
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 th...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/16/7265 |
id |
doaj-2f3efca3dec84a72b9663106d87295f1 |
---|---|
record_format |
Article |
spelling |
doaj-2f3efca3dec84a72b9663106d87295f12021-08-26T13:29:22ZengMDPI AGApplied Sciences2076-34172021-08-01117265726510.3390/app11167265Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning AlgorithmMohammad A. R. Abdeen0Ahmed Abdeen Hamed1Xindong Wu2Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi ArabiaSchool of Cybersecurity, Data Science and Computing, Norwich University, Northfield, VT 05663, USAMininglamp Academy of Sciences, Mininglamp Technology, Beijing 100864, ChinaThe 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.https://www.mdpi.com/2076-3417/11/16/7265COVID-19 infodemictext classificationTF-IDF featuresnetwork training modessupervised learningmisinformation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohammad A. R. Abdeen Ahmed Abdeen Hamed Xindong Wu |
spellingShingle |
Mohammad A. R. Abdeen Ahmed Abdeen Hamed Xindong Wu Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm Applied Sciences COVID-19 infodemic text classification TF-IDF features network training modes supervised learning misinformation |
author_facet |
Mohammad A. R. Abdeen Ahmed Abdeen Hamed Xindong Wu |
author_sort |
Mohammad A. R. Abdeen |
title |
Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm |
title_short |
Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm |
title_full |
Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm |
title_fullStr |
Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm |
title_full_unstemmed |
Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm |
title_sort |
fighting the covid-19 infodemic in news articles and false publications: the neonet text classifier, a supervised machine learning algorithm |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-08-01 |
description |
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. |
topic |
COVID-19 infodemic text classification TF-IDF features network training modes supervised learning misinformation |
url |
https://www.mdpi.com/2076-3417/11/16/7265 |
work_keys_str_mv |
AT mohammadarabdeen fightingthecovid19infodemicinnewsarticlesandfalsepublicationstheneonettextclassifierasupervisedmachinelearningalgorithm AT ahmedabdeenhamed fightingthecovid19infodemicinnewsarticlesandfalsepublicationstheneonettextclassifierasupervisedmachinelearningalgorithm AT xindongwu fightingthecovid19infodemicinnewsarticlesandfalsepublicationstheneonettextclassifierasupervisedmachinelearningalgorithm |
_version_ |
1721195111055884288 |