Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

Abstract

Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand; it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues.

Attitudes of Covid‐19 patients toward sharing their health data: A survey‐based study to understand security and privacy concerns

“…The present cross‐sectional study was conducted using an electronic researcher‐made questionnaire. This questionnaire was designed by reviewing related studies 1–3,5,7,8,12–14,20–27 and sample questionnaires related to security and privacy 15–18 …”

“…The present cross-sectional study was conducted using an electronic researcher-made questionnaire. This questionnaire was designed by reviewing related studies [1][2][3]5,7,8,[12][13][14][20][21][22][23][24][25][26][27] and sample questionnaires related to security and privacy. [15][16][17][18] Also, the opinions of two medical informatics specialists and two software engineers with at least 5 years of experience in the information technology department of medical centers were used to design a questionnaire.…”

“…So far, a huge amount of health data has been collected and shared digitally, which has recently increased greatly with the outbreak of the Covid‐19 pandemic. As the biggest challenge of the last century, this disease has disrupted public health in the world 1,2 . Moreover, Covid‐19 has threatened lives, destabilized businesses, damaged daily life, induced stress and anxiety in people, and stunned the global economy 3,4 .…”

Trust Concerns Regarding Health-Related Smartphone Apps in Collecting Personally Identifiable Information Throughout COVID-19-like Zoonosis

“…To control and track the COVID-19 trajectory, different administrations have utilized modern artificial intelligence (AI) based methods integrated with 5G technology and aerial drone devices for real-time COVID-19 tracking [3] [4]. With no specific vaccine or medicine for preventing or curing [5], currently, the best approach to avoid COVID-19 is not to get ourselves exposed to it [6]. Social distancing must be preserved between all, as recent studies [7] have shown that even an asymptomatic person may silently propagate the disease without raising any suspicion.…”