ABSTRACT
During COVID-19 pandemic, misinformation and fake news spread in social media and unauthorized websites. Publics feel anxious during the pandemic and browse to seek information related to COVID-19. Advancement of big data analytics and artificial intelligence can assist detection and classification of medical misinformation and fake news especially during pandemic. Therefore, this chapter focuses on the development of Big Medical Data Mining System called BigMed for the detection and classification of COVID-19 misinformation and fake news using machine learning. In addition, the system provides an analytical dashboard to show verified information related to the summary of new coronavirus cases, trend and forecasting, province and state breakdown, and lastly the analysis of fake news. The BigMed system also provides some reliable information about coronavirus symptoms, prevention, vaccine, etc. This system can increase awareness about COVID-19 among public to prevent critical misconceptions caused by believing on fake news and misinformation. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
Nowadays, Health misinformation and myths regarding various types of disease has spread on social media which terrified the public. During COVID-19 pandemic, misinformation and fake news outbreak increased as social media platforms play important role to enable people to view, search, and share the news as well as their point of view globally. Social media users might find difficulties in checking the validity of the news as they could not differentiate which one are the authorized news. Thus, it is too risky if people could easily be swayed by believing the news without validation. Therefore, the goal of this research is to classify the news related to COVID-19 using topic modeling and clustering. Latent Dirichlet Allocation is used for topic modeling of the fake and real news. This study can increase the awareness among social media users to reduce the risk of believing and sharing the misinformation especially during COVID-19 pandemic. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
This study aims to ascertain the factors responsible for the behavior change of consumers in Nigeria towards the use of online shopping as impacted by the COVID-19 pandemic. For this reason, two quantitative studies were conducted to find user behavior towards using online shopping before and during the COVID-19 pandemic. Questionnaire was used as the research instrument and an online survey was conducted in which 82 respondents in Nigeria participated for both studies. Both studies develop hypotheses through the integration of technology acceptance models, unified theory of acceptance and use of technology, and theory of planned behavior. The results of the study before and during COVID-19 pandemic are compared accordingly. Based on the findings of this study, recommendations were proffered in relation to the results of the various hypothesized factors. Lastly, the study gave suggestions for subsequent research. © 2022 IGI Global. All rights reserved.
ABSTRACT
The COVID-19 pandemic instigated thousands of companies' closures and affected offline retail shops. Thus, online B2C business models enable traditional offline stores to boost their sales. This study aims to explore the use of historical sales and behavioral data analytics to construct a recommendation model. A process model is proposed, which is the combination of recency, frequency, and monetary (RFM) analysis method and the k-means clustering algorithm. RFM analysis is used to segment customer levels in the company while the association rule theory and the apriori algorithm are utilized for completing the shopping basket analysis and recommending products based on the results. The proposed recommendation model provides a good marketing mix to improve sales and market responsiveness. In addition, it recommends specific products to new customers as well as specific groups of target customers. This study offered a practical business transformation case that can assist companies in a similar situation to transform their business model and improve their profits. © 2022 IGI Global. All rights reserved.