Co-authorship network and the correlation with academic performance

This paper aims to study the internal structure of the co-authorship network and the relationship between the network and the authors’ academic performance in the network. In order to conduct this research, bibliographic data of 166 authors from three top higher education institutions of Shanghai was collected and the method of social network analysis (SNA) was performed to analyze the data. In the link analysis, the centrality, egocentric network efficiency, authorities, and hubs were analyzed. In the graph cluster analysis, this paper employs clustering algorithms based on betweenness. Lastly, the Spearman correlation test was performed to analyze the relationship between academic performance and SNA metrics. This paper found that and betweenness centrality, eigenvector centrality, authority and hub position, and efficiency were significant to g-index. The research provided a glimpse of the co-authorship network's internal structure in China. Additionally, the SNA method of identifying productive scholars can also be applied to other areas, such as the network of equipment in the Industry 5.0 to help companies identify the strong and weak links in the producing process

The Ethical Issues of Location-Based Services on Big Data and IoT

Both Internet of Things (IoT) and big data are hot topics in recent years. They indeed have brought about the change of business, promoted the progress of science and technology, and facilitated the lives of human beings. IoT creates the opportunity to connect every item to the Internet, and countless science and technology have supported the achievement of this goal. LBS is one of the indispensable technologies. It brings significant benefits to the business community, the individual, the society, and the national defense. However, at the same time, an individual’s personal information is disclosed and even attacked by ‘information thieves’. An inevitable reality is that the prerequisite of getting a location service is to expose your position first. Therefore, the privacy-related ethics issues are generated, and the danger is imminent, although there are corresponding protective measures

An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews

Sentiment analysis has demonstrated its value in a range of high-stakes domains. From financial markets to supply chain management, logistics, and technology legitimacy assessment, sentiment analysis offers insights into public sentiment, actionable data, and improved decision forecasting. This study contributes to this growing body of research by offering a novel multi-view deep learning approach to sentiment analysis that incorporates non-textual features like emojis. The proposed approach considers both textual and emoji views as distinct views of emotional information for the sentiment classification model, and the results acknowledge their individual and combined contributions to sentiment analysis. Comparative analysis with baseline classifiers reveals that incorporating emoji features significantly enriches sentiment analysis, enhancing the accuracy, F1-score, and execution time of the proposed model. Additionally, this study employs LIME for explainable sentiment analysis to provide insights into the model's decision-making process, enabling high-stakes businesses to understand the factors driving customer sentiment. The present study contributes to the literature on multi-view text classification in the context of social media and provides an innovative analytics method for businesses to extract valuable emotional information from electronic word of mouth (eWOM), which can help them stay ahead of the competition in a rapidly evolving digital landscape. In addition, the findings of this paper have important implications for policy development in digital communication and social media monitoring. Recognizing the importance of emojis in sentiment expression can inform policies by helping them better understand public sentiment and tailor policy solutions that better address the concerns of the public

Smart Medical and Its Ethical Problems

With the rapid development of smart devices, big data and the Internet of Things, the concept of “smart city” has been raised and developed. Over the past decade, the concept of smart medical has received extensive attention. Currently, some simple smart medical technologies have begun to be applied, such as online appointment registration, electronic medical records, etc. This report aims to introduce the vision of “smart medical.” Based on the analytics of this study, some potential ethical problems are discovered, including privacy problems, data ownership problems, security and liability problems, and unemployment problems

Job satisfaction and turnover decision of employees in the Internet sector in the US

This paper proposes that high value on the work-life balance, compensation, career opportunity and fitness of culture and management style would improve job satisfaction. A turnover risk prediction model based on the random forest is constructed to understand the turnover risk feature and identify risk. Using a sample of 17,724 online reviews of employees from Glassdoor, the positive effect of antecedents, the job satisfaction variable as a mediator, and the unemployment rate variable as a moderator is verified. Finally, job satisfaction is identified as the most important feature for predicting turnover based on the random forest algorithm