ArticleHealthcare (Basel, Switzerland)2021
Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Prevalence and associated factors of last dental visit and teeth cleaning frequency in Bangladesh, Bhutan, and Nepal: Findings from nationally representative surveys.PLOS global public health · 2024Article
- Termbot: A Chatbot-Based Crossword Game for Gamified Medical Terminology Learning.International journal of environmental research and public health · 2023Article
- The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.JMIR infodemiology · 2023Article
- Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System.JMIR public health and surveillance · 2022Observational
- The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions.Vaccines · 2022Article
- Healthcare Provider Recommendations and Observed Changes in HPV Vaccination Acceptance during the COVID-19 Pandemic.Vaccines · 2022Article
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- Application of Internet of Things and Sensors in Healthcare.Sensors (Basel, Switzerland) · 2022Review
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- Opinions on Homeopathy for COVID-19 on Twitter.Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference · 2022Article
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- Article
- A machine learning application for raising WASH awareness in the times of COVID-19 pandemic.Scientific reports · 2022Article
- Relation of corona-specific health literacy to use of and trust in information sources during the COVID-19 pandemic.BMC public health · 2022Article
- Myth and Misinformation on COVID-19 Vaccine: The Possible Impact on Vaccination Refusal Among People of Northeast Ethiopia: A Community-Based Research.Risk management and healthcare policy · 2022Article
- Article
- Prioritization of the Target Population for Coronavirus Disease 2019 (COVID-19) Vaccination Program in Thailand.International journal of environmental research and public health · 2021Article
- COVID-19 Vaccination Intent and Willingness to Pay in Bangladesh: A Cross-Sectional Study.Vaccines · 2021Article
- Health Misinformation Detection: Approaches, Challenges and Opportunities.Inquiry : a journal of medical care organization, provision and financingReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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%,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.