Co-word Network Research Articles

Mapping the scientific structure and evolution of renewable energy for sustainable development.

The integration of renewable energy and sustainable development (RE&SD) can help overcome existing obstacles and create opportunities for renewable energy deployment to achieve sustainable development goals. In view of the limited research on science mapping and visualization analyses of RE&SD, this study sought to determine the scientific structure and evolution based on longitudinal and mapping change analysis. As an entity in the knowledge base, keyword and subject were considered essential information of documents. The co-word network was generated using SciMAT to reveal the dynamic aspects of the scientific research in the five subperiods. The thematic evolutionary analysis identified two main RE&SD thematic areas, with the current research hotspots that involved technological, environmental, sustainable energy innovation, and sustainable biofuel contributions. The alluvial diagram using MapEquation revealed significant structural changes from subject data. Clusters of subjects continued to grow, and more interdisciplinary integration was undergoing. This study provides a systematic study of RE&SD research, and the future research of RE&SD may inevitably consider renewable energy investment and renewable energy perspective approaches to achieve sustainable development goals.

Global Research Trends in Tyrosine Kinase Inhibitors: Coword and Visualization Study.

BackgroundTyrosine kinase inhibitors (TKIs) have achieved revolutionary results in the treatment of a wide range of tumors, and many studies on this topic continue to be published every year. Some of the published reviews provide great value for us to understand TKIs. However, there is a lack of studies on the knowledge structure, bibliometric analysis, and visualization results in TKIs research.ObjectiveThis paper aims to investigate the knowledge structure, hotspots, and trends of evolution of the TKIs research by co-word analysis and literature visualization and help researchers in this field to gain a comprehensive understanding of the current status and trends.MethodsWe retrieved all academic papers about TKIs published between 2016 and 2020 from the Web of Science. By counting keywords from those papers, we generated the co-word networks by extracting the co-occurrence relationships between keywords, and then segmented communities to identify the subdirections of TKIs research by calculating the network metrics of the overall and local networks. We also mapped the association network topology, including the network within and between TKIs subdirections, to reveal the association and structure among varied subdirections. Furthermore, we detected keyword bursts by combining their burst weights and durations to reveal changes in the focus of TKIs research. Finally, evolution venation and strategic diagram were generated to reveal the trends of TKIs research.ResultsWe obtained 6782 unique words (total frequency 26,175) from 5584 paper titles. Finally, 296 high-frequency words were selected with a threshold of 10 after discussion, the total frequency of which accounted for 65.41% (17,120/26,175). The analysis of burst disciplines revealed a variable number of burst words of TKIs research every year, especially in 2019 and 2020, such as HER2, pyrotinib, next-generation sequencing, immunotherapy, ALK-TKI, ALK rearrangement. By network calculation, the TKIs co-word network was divided into 6 communities: C1 (non-small–cell lung cancer), C2 (targeted therapy), C3 (chronic myeloid leukemia), C4 (HER2), C5 (pharmacokinetics), and C6 (ALK). The venation diagram revealed several clear and continuous evolution trends, such as non-small–cell lung cancer venation, chronic myeloid leukemia venation, renal cell carcinoma venation, chronic lymphocytic leukemia venation. In the strategic diagram, C1 (non-small–cell lung cancer) was the core direction located in the first quadrant, C2 (targeted therapy) was exactly at the junction of the first and fourth quadrants, which meant that C2 was developing; and C3 (chronic myeloid leukemia), C4 (HER2), and C5 (pharmacokinetics) were all immature and located in the third quadrant.ConclusionsUsing co-word analysis and literature visualization, we revealed the hotspots, knowledge structure, and trends of evolution of TKIs research between 2016 and 2020. TKIs research mainly focused on targeted therapies against varied tumors, particularly against non-small–cell lung cancer. The attention on chronic myeloid leukemia and pharmacokinetics was gradually decreasing, but the focus on HER2 and ALK was rapidly increasing. TKIs research had shown a clear development path: TKIs research was disease focused and revolved around “gene targets/targeted drugs/resistance mechanisms.” Our outcomes will provide sound and effective support to researchers, funders, policymakers, and clinicians.

A Co-word Analysis of Selected Science Education Literature: Identifying Research Trends of Scaffolding in Two Decades (2000-2019).

This study aims to identify research trends of scaffolding in the field of science education. To this end, both descriptive analysis and co-word analysis were conducted to examine the selected articles published in the Social Science Citation Index journals from 2000 to 2019. A total of 637 papers were retrieved as research samples through rounds of searching in Web of Science database. Overall, this study reveals a growing trend of science educators' academic publications about scaffolding in the recent two decades. In these sample papers, from 1,487 non-repeated keywords, we extracted 286 author-defined keywords shared by at least two studies as a benchmark dictionary. A series of co-word analyses were then conducted based on the dictionary to reveal the underlying co-occurring relationships of the words in title and abstract of the sample papers. Results showed that “scaffolding,” “support,” and “design” were the top three most frequently used keywords during 2000 and 2019. Visualization of co-word networks in each 5-year period further helps clarify both educators' common research foci and relevant research trends. Derived discussion and potential research directions are also provided.

Agent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis

The coronavirus disease has caused an ongoing pandemic worldwide since 2019. To slow the rapid spread of the virus, many countries have adopted lockdown measures. To scientifically determine the most appropriate measures and policies, agent-based simulation and modeling techniques have been employed. It can be challenging for researchers to select the appropriate tools and techniques as well as the input and output parameters. This study conducted a bibliometric analysis, especially a co-word network analysis, to classify relevant research articles into five clusters: conceptual, economic-based, organizational, policy-based, and statistical modeling. It then explained each approach and point of concern. Through this, researchers and modelers can identify the optimal approaches for their agent-based models.

Mining semantic information of co-word network to improve link prediction performance

Mining semantic information of co-word network to improve link prediction performance

A Co-Word Analysis of Global Research on Knowledge Organization: 1900-2019

The study’s objective is to analyze the structure of knowledge organization studies conducted worldwide. This applied research has been conducted with a scientometrics approach using the co-word analysis. The research records consisted of all articles published in the journals of Knowledge Organization and Cataloging & Classification Quarterly and keywords related to the field of knowledge organization indexed in Web of Science from 1900 to 2019, in which 17,950 records were analyzed entirely with plain text format. The total number of keywords was 25,480, which was reduced to 12,478 keywords after modifications and removal of duplicates. Then, 115 keywords with a frequency of at least 18 were included in the final analysis, and finally, the co-word network was drawn. BibExcel, UCINET, VOSviewer, and SPSS software were used to draw matrices, analyze co-word networks, and draw dendrograms. Furthermore, strategic diagrams were drawn using Excel software. The keywords “information retrieval,” “classification,” and “ontology” are among the most frequently used keywords in knowledge organization articles. Findings revealed that “Ontology*Semantic Web”, “Digital Library*Information Retrieval” and “Indexing*Information Retrieval” are highly frequent co-word pairs, respectively. The results of hierarchical clustering indicated that the global research on knowledge organization consists of eight main thematic clusters; the largest is specified for the topic of “classification, indexing, and information retrieval.” The smallest clusters deal with the topics of “data processing” and “theoretical concepts of information and knowledge organization” respectively. Cluster 1 (cataloging standards and knowledge organization) has the highest density, while Cluster 5 (classification, indexing, and information retrieval) has the highest centrality. According to the findings of this research, the keyword “information retrieval” has played a significant role in knowledge organization studies, both as a keyword and co-word pair. In the co-word section, there is a type of related or general topic relationship between co-word pairs. Results indicated that information retrieval is one of the main topics in knowledge organization, while the theoretical concepts of knowledge organization have been neglected. In general, the co-word structure of knowledge organization research indicates the multiplicity of global concepts and topics studied in this field globally.

Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods

Topic Characteristics of Large-Scale Online Public Opinion Based on Coword Networks and Event-Driven Methods

Consumers' Perception Structure of Food for Specified Health Uses and Food with Function Claims: Co-Word Network Analysis Focused on Association Relationships from Functional Claims

Consumers' Perception Structure of Food for Specified Health Uses and Food with Function Claims: Co-Word Network Analysis Focused on Association Relationships from Functional Claims

Menopause and Sustainable Career Outcomes: A Science Mapping Approach.

This paper provides a systematic review of the phenomenon of menopause at the workplace from a sustainable career perspective, by highlighting its major themes along with the evolution and tendencies observed in this field. A conceptual science mapping analysis based on co-word bibliographic networks was developed, using the SciMAT tool. From 1992 to 2020, 185 documents were retrieved from the Web of Science. In the first analyzed time span (1992–2002), postmenopausal women, health, and risk factors appeared to be the motor themes (well-developed and important for the structure of the discipline under focus), and disorder was an emerging or disappearing theme in the phenomenon under research. In the second studied period (2003–2013), risk and health were motor themes, menopausal symptoms was a basic or transversal theme (important for the discipline but not well-developed), coronary heart disease was a specialized theme (well-developed but less important for the structure of the research field), and postmenopausal women was an emerging or disappearing theme (both weakly developed and marginal to the field). In the third studied period (2014–2020), menopause, breast cancer, and menopausal symptoms were motor themes, Anxiety was a specialized theme and risk and body mass index were emerging or disappearing themes. Sustainability of women’s careers in the second half of life is of increasing importance given the increasing equal representation of men and women in working organizations, and the impact of the changing nature of work in the 21st century on older workers.

Knowledge Structure of the Application of High-Performance Computing: A Co-Word Analysis

As high-performance computing (HPC) plays a key role in the Fourth Industrial Revolution, the application of HPC in various industries is becoming increasingly important. Several studies have reviewed the research trends of HPC but considered only the functional aspects, causing limitations when discussing the application. Thus, this study aims to identify the knowledge structure of the application of HPC, enabling practical and policy support in various industrial fields. Co-word analysis is mainly used to establish the knowledge structure. We first collected 28,941 published papers related to HPC applications and built a co-word network that used author keywords. We performed centrality analysis and cluster analysis of the co-word network; as a result, we derived the major keywords and 18 areas of HPC applications. To validate the knowledge structure, we conducted a case study to find opportunities for HPC research plans in the research community. As a result, we discovered 17 new research topics and presented their research priorities by conducting expert interviews and Analytic Hierarchy Process. The findings of this study contribute to an understanding of the application of HPC, to exploring promising research fields for technological and social development, and to supporting research plans for successful technology commercialization.

20 Years of Research on Real Estate Bubbles, Risk and Exuberance: A Bibliometric Analysis

Since the 2008 global financial crisis, the study of real estate risks, bubbles and exuberance has attracted a lot of attention, which is of great significance to the government, enterprises and individuals. This paper carries out a statistical and bibliometric analysis of 739 studies over the past 20 years in this field, and identifies the theoretical basis, research topics and latest research hotspots in this field. Through the construction of a co-citation network and co-word network, we find that the current studies have commonly employed basic economic theories or econometric methods to detect and quantify real estate bubbles. Explosive bubbles and financialization of real estate are frontier hotspots that have gained much popularity. Based on the comparative study, we further discover that American scholars have paid more attention to topics related to the financial crisis, while Chinese scholars have studied topics more related to real estate market policies. Finally, this paper proposes several important research directions in this field for the future.

Research on CO-word network topic mining and topic differences based on haze microblog data

Some studies have shown that haze not only poses a threat to people’s health, but also affects the secretion of human hormones, making people depressed and endangering mental health. Microblog has the advantages of short content, rapid communication and convenient interaction. When the haze comes, a large number of topic microblogs related to the haze will be generated. Mining the topics of concern and psychological reactions contained in these microblogs is helpful for resource allocation and public opinion publicity in the case of haze. At present, the research of microblog topic mining in haze situation only involves a single research area, and few studies discuss the spatial differences of different regions. Based on this, this study collected the microblog data of seven provincial capitals in the severe haze areas in 2017, and used the community-based co word network method to complete a series of experimental steps, such as keyword extraction, co-occurrence matrix construction, co-word network construction and topic community detection. On this basis, we detected the topic community in the microblog data set, and analyzed the horizontal differences of topics in different cities. The results show that different cities have not only the same but also different concerns about haze. The results can provide theoretical guidance for the healthy development of cities.

Tracking the dynamics of co-word networks for emerging topic identification

Identifying emerging topics has been an essential study for nations to develop strategic priorities, for enterprises to create business strategies, and for institutions to define research areas. However, how to characterize emerging topics effectively and comprehensively is still very challenging. This study proposes a framework for identifying emerging topics based on a dynamic co-word network analysis, which integrates a link prediction model with machine learning techniques. Time-sliced co-word networks are weighted according to the frequency of terms' co-occurrence. A back-propagation neural network is used to forecast a future network by predicting linkages among unconnected nodes based on existing links. Four indicators are then used to sort out potential candidates of emerging topics in the predicted network. A case study on information science demonstrates the reliability of the proposed methodology, followed by subsequent empirical and expert validations.

A comparative analysis on risk communication between international and Chinese literature from the perspective of\xa0knowledge domain visualization

BackgroundThe outbreak of coronavirus disease (COVID-19) severely damaged and endangered people’s lives at the end of 2019. Risk communication plays an important role in the response to it successfully, which has been appreciated by the World Health Organization. Therefore, a comprehensive analysis of risk communication research is necessary, which can understand current research hotspots and reveal new trends.MethodsIn this study, we collected 1134 international articles from the Web of Science database and 3983 Chinese articles from the China National Knowledge Infrastructure database. Bibliometric and mapping knowledge domain analysis methods were used for temporal distribution analysis, cooperation network analysis, co-word network analysis, and burst detection analysis.ResultsThe first article in this field was published by western scholars earlier, while the first Chinese article in 2002. Research institutions mainly come from universities. The USA plays a key role in this field. Chinese scholars had a closer cooperation network, but there was less cooperation among domestic institutions. Risk perception, trust, risk management, and risk information had always been the research hotspots in this academic. Trust, sentiment research, and public risk events were essential directions for the future. There are 25 burst words for international articles, while 11 burst words for Chinese articles from 2000 to 2020.ConclusionsIn summary, both domestic and international researchers are concerned about risk communication, risk perception, trust, and risk information. International research on risk communication is systematic and comprehensive relatively. However, Chinese scholars take severe acute respiratory syndrome as the research background and reviewing foreign knowledge as the research starting point. With the purpose of practical and applied research based on a public emergency, the risk communication research lacks continuity in Chinese academy in the past years.

The Research Framework and Evolution of Service Robots

ABSTRACT With the development of emerging science and technology, service robots have attracted more attention from scholars. However, it is still lack of comprehensive analysis of these studies from a quantitative perspective. This paper combines quantitative and qualitative methods to analyze the researches of service robots from the perspective of systematic literature review. Based on the clustering analysis of the co-citation network and the co-word network, this paper identifies the main topics and proposes a research framework of service robots which divides the existing researches into five levels. Then, through the bursts detection, this study analyzes hot spots and development of the researches on service robots. Based on the timeline analysis, this paper divides the evolution of the researches into three stages. Besides, this study analyzes the researches in the last three years to determine the recent research priorities and trends, so as to provide a reference for scholars and managers.

KE-CNN: A new social sensing method for extracting geographical attributes from text semantic features and its application in Wuhan, China

Social sensing is an analytical method to study the interaction between human and space through extracting reliable information from massive volunteered information data. During the ongoing COVID-19 pandemic, there are a large number of Internet social sensing data. However, most of them lack geographic attribute. In order to resolve this problem, this paper proposes a convolutional neural network geographic classification model based on keyword extraction and synonym substitution (KE-CNN) which could determine the geographic attribute by extracting the semantic features from text data. Besides, we realizes the non-contact pandemic social sensing and construct the co-word complex network by capturing the spatiotemporal behaviour of a large number of people. Our research found that (1) mining co-word network can obtain most public opinion information of pandemic events, (2) KE-CNN model improves the accuracy by 5%–15% compared with the traditional machine learning method. Through this method, we could effectively establish medical, catering, railway station, education and other types of text feature set, supplement the missing spatial data tags, and achieve a good geographical seamless social sensing.

Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

Topic structure and evolution patterns of documentary heritage preservation and conservation research in China

PurposeThis study aims to identify the prominent topics, the distribution and association characteristics of topics and the topic evolutionary trends of Documentary Heritage Preservation and Conservation (DHPAC) research in China.Design/methodology/approachKeywords of relevant papers in China National Knowledge Infrastructure (CNKI) were extracted as the data source in this study. First, frequency and co-occurrence of keywords of the selected papers were obtained by using SATI. Second, co-word network indicators were calculated with the Pajek software. Then, VOSviewer was applied to optimize the visualization of the sub-communities. Finally, a topics evolution map of this research field was implemented by CorTexT.FindingsThe research topics of DHPAC research in China were unbalanced but distinct. Topics of DHPAC research in China possessed inconspicuous orientation and consistency. The core topics had less influence on the overall network. A research system had formed with archival conservation and ancient books conservation as the core research directions. Research in this field had formed four continuous evolutionary paths about ancient books conservation, salvage conservation, archival conservation and archives conservation technology science with topics fusion and differentiation coexisting. Attentions on “ancient books conservation”, “paper relics conservation”, “electronic record”, “digitization”, “minority”, “documents in the republic of China” had increased during the past two decades and new hot topics of DHPAC research kept appearing in China.Originality/valueThis study synthesized and analyzed the research results of DHPAC research in China from a more comprehensive perspective and revealed the topic structure and longitudinal evolution process intuitively with co-word analysis and social network analysis, which can assist researchers to improve research systematization, discover new research directions and seek cooperative research path.

Green supply chain management in the platform economy: a bibliometric analysis

ABSTRACT This paper aims to provide a bibliometric analysis of green supply chain management in platform economy (GSCM-PE) literature. Based on the bibliometric approach, a descriptive analysis is engaged in the literature published from 2003 to 2020 in Web of Science. There are 1404 publications in this field of GSCM-PE. The number of publications has increased steadily since 2013. China is an essential contributor to this field, with the largest number of publications (356), followed by the USA (175) and England (116). However, China's total citation is 0.5 times that of the USA, which indicates that China needs further strengthened in quality. In addition, the cooperation between European and American countries is relatively active. Six clusters are identified from co-word network, and their main conclusions are as follows: (i) based on Internet technology, the application of SCM in business is the hottest topic and (ii) network risk and security requires further exploration.

User Experience & Usability of Driving: A Bibliometric Analysis of 2000-2019

ABSTRACT Driving is the most complex human-computer interaction for many individuals. The user experience (UX) and usability of driving are key issues in determining driving safety and user acceptance. Therefore several studies on both issues have been performed toward formulating beneficial driver-vehicle interactions and for future intelligent vehicles and autonomous environments. To provide an insight into these studies, this present study conducts bibliometric analyses to obtain information about when and where the research was conducted by whom and how mainstream content has evolved over the years. BibExcel and CiteSpace were utilized for the performance and co-word network analyses. A total of 2498 documents from 2000– 2019 in the field were filtered from the “Core Set” of Web of Science (WoS) for quantitative analysis. This study offers researchers a comprehensive understanding of UX and usability of driving studies in the last decades and research directions for the future.