Towards a Bibliometric Mapping of Network Public Opinion Studies

Cloud Gamification: Bibliometric Analysis and Research Advances

Research on gamification in the cloud has been increasing in recent years. The main objective of this work was to analyse the advances and progress reported in the scientific literature published internationally in cloud gamification from a bibliometric perspective. The scientific production in this field was identified using the Web of Science (WoS) database. The analysis was carried out with the support of the VOSviewer software, version 1.6.18, developed by van Eck and Waltman, for the graphical visualisation of bibliometric networks. The study period covered the time from the first publication on the subject in 2012 to 31 July 2022, with 108 documents detected. The most prolific author was Jacub Swacha from the University of Szczecin, Poland. Forty-seven countries published on Cloud Gamification, with Spain and Italy being the countries with the highest scientific production. The most productive organisations were Bucharest University of Economic Studies, Complutense University of Madrid, Liverpool John Moores University and the University of Szczecin. The journal with the highest output was Information. The groups in the producing countries, the authors, the organisations to which they belonged and the thematic areas of the studies were identified, as well as their evolution over time.

How the V4 Nations Handle the Idea of Smart Cities

Smart city is a term that includes digital, information, and communication technologies that contribute to increasing the level and quality of life in individual cities. It focuses primarily on the efficient use of existing resources but also on the discovery of new ones, with the goal of lowering energy consumption while also reducing environmental impact and optimizing traffic in specific areas of the city. This concept is increasingly coming to the fore. Thus, the aim of this article was to determine the level of involvement of Slovak, Czech, Polish, and Hungarian authors in solutions for Smart cities using Web of Science data. The analysis of countries that form the Visegrad Four (V4) region reveals how the region ranks compared to other countries that are actively involved in Smart cities based on VosViewer. To map a specific region of countries, it is necessary to first understand the underlying causes of the problem worldwide. Then, the status of the authors, the number of articles and citations, and universities may be actively discussed and graphically depicted for each nation in Visegrad. Based on the discovered results, academics can identify the contributors and institutions that have solved the issue individually or in co-authorships over a long period. The findings provide data for future testing of selected dependencies and a platform for creating a scientific model to rank countries. In addition, the authorities may focus on identified clusters of key areas that are an essential part of Smart cities and provide a higher quality of life in their city for the people.

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

The aim is to clarify the evolution mechanism of Network Public Opinion (NPO) in public emergencies. This work makes up for the insufficient semantic understanding in NPO-oriented emotion analysis and tries to maintain social harmony and stability. The combination of the Edge Computing (EC) and Deep Learning (DL) model is applied to the NPO-oriented Emotion Recognition Model (ERM). Firstly, the NPO on public emergencies is introduced. Secondly, three types of NPO emergencies are selected as research cases. An emotional rule system is established based on the One-Class Classification (OCC) model as emotional standards. The word embedding representation method represents the preprocessed Weibo text data. Convolutional Neural Network (CNN) is used as the classifier. The NPO-oriented ERM is implemented on CNN and verified through comparative experiments after the CNN's hyperparameters are adjusted. The research results show that the text annotation of the NPO based on OCC emotion rules can obtain better recognition performance. Additionally, the recognition effect of the improved CNN is significantly higher than the Support Vector Machine (SVM) in traditional Machine Learning (ML). This work realizes the technological innovation of automatic emotion recognition of NPO groups and provides a basis for the relevant government agencies to handle the NPO in public emergencies scientifically.

Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study

Public health emergency management has been one of the main challenges of social sustainable development since the beginning of the 21st century. Research on public health emergency management is becoming a common focus of scholars. In recent years, the literature associated with public health emergency management has grown rapidly, but few studies have used a bibliometric analysis and visualization approach to conduct deep mining and explore the characteristics of the public health emergency management research field. To better understand the present status and development of public health emergency management research, and to explore the knowledge base and research hotspots, the bibliometric method and science mapping technology were adopted to visually evaluate the knowledge structure and research trends in the field of public health emergency management studies. From 2000 to 2020, a total of 3723 papers related to public health emergency management research were collected from the Web of Science Core Collection as research data. The five main research directions formed are child prevention, mortality from public health events, public health emergency preparedness, public health emergency management, and coronavirus disease 2019 (COVID-19). The current research hotspots and frontiers are climate change, COVID-19 and related coronaviruses. Further research is needed to focus on the COVID-19 and related coronaviruses. This study intends to contribute inclusive support to related academia and industry in the aspects of public health emergency management and public safety research, as well as research hotspots and future research directions.