Shams Abdullah Bin, Hoque Apu Ehsanul, Rahman Ashiqur, Sarker Raihan Md Mohsin, Siddika Nazeeba, Preo Rahat Bin, Hussein Molla Rashied, Mostari Shabnam, Kabir Russell
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada.
Institute of Quantitative Health Science, Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA.
Healthcare (Basel). 2021 Feb 3;9(2):156. doi: 10.3390/healthcare9020156.
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%, 1-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.
诸如来自不可信来源的关于2019冠状病毒病（COVID-19）药物、疫苗接种或治疗方法的错误信息已对公众健康造成了严重后果。当局已部署了多种监测工具来检测并减缓网上错误信息的迅速传播。网上有大量未经证实的信息，目前还没有实时工具可在用户通过网络搜索引擎进行在线健康查询时提醒其注意虚假信息。为了弥补这一差距，我们提出了一种网络搜索引擎错误信息通知扩展程序（SEMiNExt）。自然语言处理（NLP）和机器学习算法已成功集成到该扩展程序中。这使得SEMiNExt能够从搜索栏读取用户查询，对查询的真实性进行分类，并实时向用户通知查询的真实性，以防止错误信息的传播。我们的结果表明，当80%的数据用于训练时，人工神经网络（ANN）下的SEMiNExt效果最佳，准确率为93%，F1分数为92%，精确率为92%，召回率为93%。此外，即使训练数据量很小，ANN也能以非常高的准确率进行预测。这对于从网上可用的小数据样本中早期检测新的错误信息非常重要，因为这可以显著减少错误信息的传播并最大限度地提高公众健康安全。SEMiNExt方法通过实时显示错误信息通知，为改进在线健康管理系统带来了可能性，从而实现更安全的基于网络的健康相关问题搜索。
