Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions

Identifying trends and flows in Communication and Information Processing by means of keyword network analysis

The purpose of this paper is to identify influential themes and knowledge flows in the area of communications and information processing and suggest trends that are likely to make (or continue making) an impact. We applied keyword network analysis on articles whose keywords match the themes of the International Conference on Communication and Information Processing, collected through the Thompson Reuters’ Web of Science and studied the articles’ thematic interconnections and their dynamics. The keyword network was found to be clustered around the themes cloud, data, mobile, security, semantic and social. Security and embeddedness are found to be the most dominant topics, common to all groups. Design and performance are key influencers of thematic flows and data mining/analysis are close to all nodes/keywords and therefore most popular. Big data, data fusion/integration and e-government are themes identified as potentially strong future influencers

Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy

The thematic evolution of research on Mobile Information Literacy between 2006 and 2019 in the field of Information Literacy, learning and mobile technologies is analysed in an international context. For this purpose, the relevant bibliographic references from five databases (ERIC, LISA, LISTA, Scopus and WOS) were retrieved. To systematize the keywords, high dimensionality is reduced by means of a term-based process. Fields, topics, sub-topics and top terms are defined. The main top-terms and their relationships are analysed applying the fractional counting methodology using VOSViewer software. Fifteen major themes were set, which were grouped into six clusters to identify the main thematic trends during the period under review: IL and e-learning, Mobile devices and competencies, Ethics, Library and e-resources, Educational technology and Technological environment. The convergence of IL and e-learning, the growth of e-literacy, the increasing relationship between mobile devices and information competencies, as well as that of libraries and e-resources, are thus detected. In conclusion, there is evidence of a growing interdisciplinarity in the scientific publications on Mobile Information Literacy, which interrelates the studies of information and digital literacy with e-learning and mobile technologies.This research is part of the R&D project “Innovation and training in the information competencies of university lecturers and students in the social sciences. Model for the development of programs in the mobile environment” (CSO2016-80147-R), funded by the Spanish Ministry of Economy, Industry and Competitiveness

Visualizing Hot and Emerging Topics in Biochemistry and Molecular Biology in Iran

The purpose of this descriptive research was to identify hot and emerging topics in Biochemistry and Molecular Biology in Iran and to map the intellectual structure of this field in a ten-year period. The intellectual structure of the field of Biochemistry and Molecular Biology in Iran was studied by analyzing co-occurrences of keywords and cited references. The research population of this study was all research and review papers of Iranian researchers published in journals indexed by the Web of Science database from 2008 to 2017. The collected data from Web of Science were analyzed by the CiteSpace Software in order to map the intellectual structure of this field. The results showed that the keywords such as gene expression, protein, in vitro, oxidative stress, binding, apoptosis and cell were among the hot research topics in Iran and terms such as chitosan, nanocomposite, antibacterial activity, dynamics molecules, stem cells, mesenchymal stem cells and immobilization have been indicative of the emerging topics in Iranian research in the studied time period. Increasing publications in the field of Biochemistry and Molecular Biology in Iran at the international level and its inclusion in the country's research priorities led us to conduct a scientometric study of this research area. Therefore, due to the hot and emerging topics identified in this research, such studies can be used as a road map for the country's large-scale scientific planning and policy

Information Literacy Trends in Higher Education (2006-2019): Visualizing the Emerging Field of Mobile Information Literacy

The thematic evolution of research on Mobile Information Literacy (MoIL) between 2006 and 2019 in the field of Information Literacy, learning and mobile technologies is analysed in an international context. For this purpose, the relevant bibliographic references from five databases (ERIC, LISA, LISTA, Scopus and WOS) were retrieved. To systematize the keywords, high dimensionality is reduced by means of a term-based process. Fields, topics, sub-topics and top terms are defined. The main top-terms and their relationships are analysed applying the fractional counting methodology using VOSViewer software. Fifteen major themes were set, which were grouped into six clusters to identify the main thematic trends during the period under review: IL & e-learning, Mobile devices & competencies, Ethics, Library & e-resources, Educational technology and Technological environment. The convergence of IL and elearning, the growth of e-literacy, the increasing relationship between mobile devices and information competencies, as well as that of libraries and e-resources, are thus detected. In conclusion, there is evidence of a growing interdisciplinarity in the scientific publications on Mobile Information Literacy, which interrelates the studies of information and digital literacy with e-learning and mobile technologies

Visualizing collaboration characteristics and topic burst on international mobile health research: Bibliometric analysis

Background: In the last few decades, mobile technologies have been widely adopted in the field of health care services to improve the accessibility to and the quality of health services received. Mobile health (mHealth) has emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported. Objective: The purpose of this study was to analyze the current state of research, including publication outputs, in the field of mHealth to uncover in-depth collaboration characteristics and topic burst of international mHealth research. Methods: The authors collected literature that has been published in the last 20 years and indexed by Thomson Reuters Web of Science Core Collection (WoSCC). Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author, institution, and country collaboration level. The temporal visualization map of burst terms was drawn, and the co-occurrence matrix of these burst terms was analyzed by hierarchical cluster analysis and social network analysis. Results: A total of 2704 bibliographic records on mHealth were collected. The earliest paper centered on mHealth was published in 1997, with the number of papers rising continuously since then. A total of 21.28% (2318/10,895) of authors publishing mHealth research were first author, whereas only 1.29% (141/10,895) of authors had published one paper. The total degree of author collaboration was 4.42 (11,958/2704) and there are 266 core authors who have collectively published 53.07% (1435/2704) of the total number of publications, which means that the core group of authors has fundamentally been formed based on the Law of Price. The University of Michigan published the highest number of mHealth-related publications, but less collaboration among institutions exits. The United States is the most productive country in the field and plays a leading role in collaborative research on mHealth. There are 5543 different identified keywords in the cleaned records. The temporal bar graph clearly presents overall topic evolutionary process over time. There are 12 important research directions identified, which are in the imbalanced development. Moreover, the density of the network was 0.007, a relatively low level. These 12 topics can be categorized into 4 areas: (1) patient engagement and patient intervention, (2) health monitoring and self-care, (3) mobile device and mobile computing, and (4) security and privacy. Conclusions: The collaboration of core authors on mHealth research is not tight and stable. Furthermore, collaboration between institutions mainly occurs in the United States, although country collaboration is seen as relatively scarce. The focus of research topics on mHealth is decentralized. Our study might provide a potential guide for future research in mHealth.Fundamental Research Funds for the Central Universities

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis

BackgroundLung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction.ObjectiveThis study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots.ResultsA total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations).ConclusionsResearch related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities

COVID-19 and Pregnancy: Citation Network Analysis and Evidence Synthesis.

Background: COVID-19 spread quickly around the world shortly after the first outbreaks of the new coronavirus disease at the end of December 2019, affecting all populations, including pregnant women. Objective: The aim of this study was to analyze the relationship between different publications on COVID-19 in pregnancy and their authors through citation networks, as well as to identify the research areas and to determine the publication that has been the most highly cited. Methods: The search for publications was carried out through the Web of Science database using terms such as “pregnancy,” “SARS-CoV-2,” “pregnant,” and “COVID-19” for the period between January and December 2020. Citation Network Explorer software was used for publication analysis and VOSviewer software was used to construct the figures. This approach enabled an in-depth network analysis to visualize the connections between the related elements and explain their network structure. Results: A total of 1330 publications and 5531 citation networks were identified in the search, with July being the month with the largest number of publications, and the United States, China, and England as the countries with the greatest number of publications. The most cited publication was “Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records” by Chen and colleagues, which was published in March 2020. Six groups identified as being close in the citation network reflect multidisciplinary research, including clinical characteristics and outcomes in pregnancy, vertical transmission, delivery mode, and psychological impacts of the pandemic on pregnant women. Conclusions: Thousands of articles on COVID-19 have been published in several journals since the disease first emerged. Identifying relevant publications and obtaining a global view of the main papers published on COVID-19 and pregnancy can lead to a better understanding of the topic. With the accumulation of scientific knowledge, we now know that the clinical features of COVID-19 during pregnancy are generally similar to those of infected nonpregnant women. There is a small increase in frequency of preterm birth and cesarean birth, related to severe maternal illness. Vaccination for all pregnant women is recommended. Several agents are being evaluated for the treatment of COVID-19, but with minimal or no information on safety in pregnancy. These results could form the basis for further research. Future bibliometric and scientometric studies on COVID-19 should provide updated information to analyze other relevant indicators in this field.post-print1277 K

Knowledge management and social media: A scientometrics survey

The purpose of this research is to study the role of the social media for knowledge sharing. The study presents a comprehensive review of the researches associated with the effect of knowledge management in social media. The study uses Scopus database as a primary search engine and covers 1858 of highly cited articles over the period 1994-2019. The records are statistically analyzed and categorized in terms of various criteria using an open source software package named R. The findings show that researches have grown exponentially during the recent years and the trend has continued at relatively stable rates. Based on the survey, knowledge management is the keyword which has carried the highest citations followed by social media and social networking. Among the most cited articles, papers published by researchers in United States have received the highest citations, followed by United Kingdom and China

Mapping the knowledge domain of green procurement: a review and bibliometric analysis.

The goal of green procurement, also called green purchasing, is to reduce waste and improve operational efficiencies to enhance sustainability. Although this practice has gained importance in recent years and garnered significant scholarly attention, there is a lack of bibliometric studies evaluating the green procurement field. To close this gap, we leverage bibliometrics to comprehensively summarize the literature and identify existing research hotspots and trends. Specifically, we employ bibliometric tools to analyze keywords, identify influential authors, universities, and research areas and reveal the most important publications in terms of citations. The analysis shows that sustainable development, sustainability, green supply chain management, and green public procurement are core topics related to green procurement. The co-citation analysis further reveals five important research clusters in the literature, namely green public procurement, green supply chain management, green supplier selection and evaluation of green performance, networked sustainable procurement, and green procurement in the construction sector. This study makes a contribution to the green procurement literature by summarizing this quickly growing field and providing timely guidance as to future research directions