Version 1
: Received: 17 June 2021 / Approved: 18 June 2021 / Online: 18 June 2021 (14:48:31 CEST)
Version 2
: Received: 11 July 2021 / Approved: 12 July 2021 / Online: 12 July 2021 (11:58:57 CEST)
Version 3
: Received: 24 July 2021 / Approved: 26 July 2021 / Online: 26 July 2021 (12:06:04 CEST)
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.
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.
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.
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.
Abstract
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 text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is 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.
Keywords
COVID-19 Infodemic; Text Classification; TFIDF Features; Network Training modes; Supervised Learning; Misinformation; News Classification; False Publications; PubMed; Anomaly Detection
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Ahmed Hamed
Commenter's Conflict of Interests: Author