University of Oulu

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

Web search engine misinformation notifier extension (SEMiNExt) : a machine learning based approach during COVID-19 pandemic

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Author: Shams, Abdullah Bin1; Apu, Ehsanul Hoque2,3; Rahman, Ashiqur4;
Organizations: 1The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
2Institute of Quantitative Health Science, Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
3The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway
4Department of Computer Science, Northern Illinois University, DeKalb, IL 60115-2828, USA
5Department of Biomedical Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
6Center for Environmental and Respiratory Health Research (CERH), Faculty of Medicine, University of Oulu, 90014 Oulu, Finland
7Department of Electrical and Electronic Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
8Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh
9Aspire to Innovate (a2i) Programme, ICT Division, Dhaka 1207, Bangladesh
10School of Allied Health, Faculty of Health, Education , Medicine and Social Care, Anglia Ruskin University, Chelmsford CM1 1SQ, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.4 MB)
Persistent link:
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2021
Publish Date: 2021-04-15


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.

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Series: Healthcare
ISSN: 2227-9032
ISSN-E: 2227-9032
ISSN-L: 2227-9032
Volume: 9
Issue: 2
Article number: 156
DOI: 10.3390/healthcare9020156
Type of Publication: A1 Journal article – refereed
Field of Science: 3141 Health care science
Dataset Reference: The novel search engine misinformation notifier extension (SEMiNExt) introduced and presented in this original paper have been stored in the GitHub repository:
Copyright information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (