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

06/25/2021
by   Abdullah Bin Shams, et al.
0

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.

READ FULL TEXT
research
07/06/2020

Go local: The key to controlling the COVID-19 pandemic in the post lockdown era

The UK government announced its first wave of lockdown easing on 10 May ...
research
04/21/2020

Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan

[Background] Two clusters of coronavirus disease 2019 (COVID-19) were co...
research
04/22/2020

OUTBREAK: A user-friendly georeferencing online tool for disease surveillance

The current COVID-19 pandemic has already claimed more than 100,000 vict...
research
12/05/2018

Machine-learned epidemiology: real-time detection of foodborne illness at scale

Machine learning has become an increasingly powerful tool for solving co...
research
08/12/2020

The Past, Present, and Future of COVID-19: A Data-Driven Perspective

Epidemics and pandemics have ravaged human life since time. To combat th...
research
06/13/2023

ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading

With the rapid growth and spread of online misinformation, people need t...
research
10/17/2021

A Deep Learning-based Approach for Real-time Facemask Detection

The COVID-19 pandemic is causing a global health crisis. Public spaces n...

Please sign up or login with your details

Forgot password? Click here to reset