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

Shams, Abdullah B. and Hoque Apu, Ehsanul and Rahman, Ashiqur and Sarker Raihan, Md Mohsin and Siddika, Nazeeba and Preo, Rahat B. and Hussein, Molla R. and Mostari, Shabnam and Kabir, Russell (2021) Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic. Healthcare, 9 (2). p. 156. ISSN 2227-9032

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Official URL: http://dx.doi.org/10.3390/healthcare9020156

Abstract

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.

Item Type: Journal Article
Keywords: COVID-19, public health misinformation, web search engine, notifier extension, natural language processing, machine learning, artificial neural network
Faculty: Faculty of Health, Education, Medicine & Social Care
COVID-19 Research Collection
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 05 Feb 2021 10:25
Last Modified: 05 Feb 2021 10:25
URI: https://arro.anglia.ac.uk/id/eprint/706248

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