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Article

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

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The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
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Institute of Quantitative Health Science, Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
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The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway
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Department of Computer Science, Northern Illinois University, DeKalb, IL 60115-2828, USA
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Department of Biomedical Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
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Center for Environmental and Respiratory Health Research (CERH), Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
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Department of Electrical and Electronic Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
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Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh
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Aspire to Innovate (a2i) Programme, ICT Division, Dhaka 1207, Bangladesh
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School of Allied Health, Faculty of Health, Education , Medicine and Social Care, Anglia Ruskin University, Chelmsford CM1 1SQ, UK
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Authors to whom correspondence should be addressed.
Equal contribution and joint first author.
Equal contribution and joint second author.
Academic Editor: Yu-Jun Zheng
Healthcare 2021, 9(2), 156; https://doi.org/10.3390/healthcare9020156
Received: 25 December 2020 / Revised: 28 January 2021 / Accepted: 28 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Healthcare Resource Management in Large-Scale Epidemics)
Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues. View Full-Text
Keywords: COVID-19; public health misinformation; web search engine; notifier extension; natural language processing; machine learning; artificial neural network COVID-19; public health misinformation; web search engine; notifier extension; natural language processing; machine learning; artificial neural network
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MDPI and ACS Style

Shams, A.B.; Hoque Apu, E.; Rahman, A.; Sarker Raihan, M.M.; Siddika, N.; Preo, R.B.; Hussein, M.R.; Mostari, S.; Kabir, R. Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic. Healthcare 2021, 9, 156. https://doi.org/10.3390/healthcare9020156

AMA Style

Shams AB, Hoque Apu E, Rahman A, Sarker Raihan MM, Siddika N, Preo RB, Hussein MR, Mostari S, Kabir R. Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic. Healthcare. 2021; 9(2):156. https://doi.org/10.3390/healthcare9020156

Chicago/Turabian Style

Shams, Abdullah B., Ehsanul Hoque Apu, Ashiqur Rahman, Md. M. Sarker Raihan, Nazeeba Siddika, Rahat B. Preo, Molla R. Hussein, Shabnam Mostari, and Russell Kabir. 2021. "Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic" Healthcare 9, no. 2: 156. https://doi.org/10.3390/healthcare9020156

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