This page has only limited features, please log in for full access.

Unclaimed
Xieling Chen
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong Special Administrative Region

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Review
Published: 17 June 2021 in Neurocomputing
Reads 0
Downloads 0

Knowledge graph as a research topic is increasingly popular to represent structural relations between entities. Recent years have witnessed the release of various open-source and enterprise-supported knowledge graphs with dramatic growth in applying knowledge representation and reasoning into different areas like natural language processing and computer vision. This study aims to comprehensively explore the status and trends – particularly the thematic research structure – of knowledge graphs. Specifically, based on 386 research articles published from 1991 to 2020, we conducted analyses in terms of the (1) visualization of the trends of annual article and citation counts, (2) recognition of major institutions, countries/regions, and publication sources, (3) visualization of scientific collaborations of major institutions and countries/regions, and (4) detection of major research themes and their developmental tendencies. Interest in knowledge graph research has clearly increased from 1991 to 2020 and is continually expanding. China is the most prolific country in knowledge graph research. Moreover, countries/regions and institutions that have higher levels of international collaboration are more impactful. Several widely studied issues such as knowledge graph embedding, search and query based on knowledge graphs, and knowledge graphs for intangible cultural heritage are highlighted. Based on the results, we further summarize perspective directions and suggestions for researchers, practitioners, and project managers to facilitate future research on knowledge graphs.

ACS Style

Xieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing 2021, 1 .

AMA Style

Xieling Chen, Haoran Xie, Zongxi Li, Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing. 2021; ():1.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. 2021. "Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades." Neurocomputing , no. : 1.

Article
Published: 05 May 2021 in Cognitive Computation
Reads 0
Downloads 0

Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide insights into the issues addressed during the past decade and the potential future of sentic computing. We combined bibliometric analysis and structural topic modeling to examine sentic computing literature in various aspects, including the tendency of annual article count, top journals, countries/regions, institutions, and authors, the scientific collaborations between major contributors, as well as the major topics and their tendencies. We obtained interesting and meaningful findings. For example, sentic computing has attracted growing interest in academia. In addition, Cognitive Computation and Nanyang Technological University were found to be the most productive journal and institution in publishing sentic computing studies, respectively. Moreover, important issues such as cyber issues and public opinion, deep neural networks and personality, financial applications and user profiles, and affective and emotional computing have been commonly addressed by authors focusing on sentic computing. Our study provides a thorough overview of sentic computing, reveals major concerns among scholars during the past decade, and offers insights into the future directions of sentic computing research.

ACS Style

Xieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation 2021, 1 -24.

AMA Style

Xieling Chen, Haoran Xie, Gary Cheng, Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation. 2021; ():1-24.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. 2021. "A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis." Cognitive Computation , no. : 1-24.

Review
Published: 26 April 2021 in Sustainability
Reads 0
Downloads 0

Computers in Human Behavior (CHB) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more than 35 years. This review provides an overview of the status, trends, and particularly the thematic structure of the CHB by adopting bibliometrics and structural topic modeling on 5957 studies. Specifically, we analyzed the trend of publications, identified major institutions and countries/regions, detected scientific collaboration patterns, and uncovered important topics. Significant findings were presented. For example, the contribution of the USA and Open University of Netherlands was highlighted. Important research topics such as e-commerce, social interactions and behaviors, public opinion and social media, cyberbullying, online sexual issues, and game and gamification were identified. This review contributes to the CHB community by justifying the interest in human behavior issues concerning computer use and identifying future research lines on this topic.

ACS Style

Xieling Chen; Di Zou; Haoran Xie; Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability 2021, 13, 4859 .

AMA Style

Xieling Chen, Di Zou, Haoran Xie, Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability. 2021; 13 (9):4859.

Chicago/Turabian Style

Xieling Chen; Di Zou; Haoran Xie; Gary Cheng. 2021. "A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics." Sustainability 13, no. 9: 4859.

Review
Published: 17 April 2021 in Asia Pacific Education Review
Reads 0
Downloads 0

Massive Open Online Courses (MOOCs) have become a popular learning mode in recent years, especially since the outbreak of COVID-19 in late 2019, which had resulted in a significant increase in associated research. This paper presents a bibliometric review of 1078 peer-reviewed MOOC studies between 2008 and 2019. These papers are extracted from three influential databases, the Web of Science (WOS), Scopus, and the Education Resources Information Center (ERIC). The MOOC literature analysis with a bibliometric approach identified the research trends, journals, countries/regions, and institutions with high H-index, scientific collaborations, research topics, topic distributions of the prolific countries/regions and institutions, and annual topic distributions, after which the representative research and research implications were discussed. This review gives researchers a deep and comprehensive understanding of current MOOC research and identifies potential research topics and collaborative partners, which supports MOOC-related future research.

ACS Style

Caixia Liu; Di Zou; Xieling Chen; Haoran Xie; Wai Hong Chan. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review 2021, 1 -20.

AMA Style

Caixia Liu, Di Zou, Xieling Chen, Haoran Xie, Wai Hong Chan. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review. 2021; ():1-20.

Chicago/Turabian Style

Caixia Liu; Di Zou; Xieling Chen; Haoran Xie; Wai Hong Chan. 2021. "A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019)." Asia Pacific Education Review , no. : 1-20.

Journal article
Published: 07 January 2021 in Neural Computing and Applications
Reads 0
Downloads 0

The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009–2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that “electroencephalogram,” “brain–computer interface,” “classification,” “support vector machine,” “electroencephalography,” and “signal” were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain–machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

ACS Style

Xieling Chen; Xiaohui Tao; Fu Lee Wang; Haoran Xie. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications 2021, 1 -39.

AMA Style

Xieling Chen, Xiaohui Tao, Fu Lee Wang, Haoran Xie. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications. 2021; ():1-39.

Chicago/Turabian Style

Xieling Chen; Xiaohui Tao; Fu Lee Wang; Haoran Xie. 2021. "Global research on artificial intelligence-enhanced human electroencephalogram analysis." Neural Computing and Applications , no. : 1-39.

Review article
Published: 14 October 2020 in Computers and Education: Artificial Intelligence
Reads 0
Downloads 0

With the rapid development of artificial intelligence (AI) technologies and a continuously growing interest in their application in educational contexts, there has been significant growth in the scientific literature in relation to the application of AI in education (AIEd). This study aims to present multiple perspectives on the development of AIEd in terms of relevant grants, conferences, journals, software tools, article trends, top issues, institutions, and researchers to provide an overview of AIEd for its further development and implementation. With this study, we contribute to the research field in terms of enabling educators and scholars to understand the status and development of relevant grants and publications concerning AIEd. Also, findings concerning active actors can help educators and scholars identify the active researchers and institutions in the research on AIEd. Furthermore, researchers and educators are able to identify relevant journals and be more aware of major issues in AIEd studies. In addition, we also highlight the significance and necessity of the launch of the new Elsevier journal AIEd-related journal named Computers & Education: Artificial Intelligence.

ACS Style

Xieling Chen; Haoran Xie; Gwo-Jen Hwang. A multi-perspective study on Artificial Intelligence in Education: grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence 2020, 1, 100005 .

AMA Style

Xieling Chen, Haoran Xie, Gwo-Jen Hwang. A multi-perspective study on Artificial Intelligence in Education: grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence. 2020; 1 ():100005.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Gwo-Jen Hwang. 2020. "A multi-perspective study on Artificial Intelligence in Education: grants, conferences, journals, software tools, institutions, and researchers." Computers and Education: Artificial Intelligence 1, no. : 100005.

Review article
Published: 07 September 2020 in Computers and Education: Artificial Intelligence
Reads 0
Downloads 0

Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners’ answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners’ during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.

ACS Style

Xieling Chen; Haoran Xie; Di Zou; Gwo-Jen Hwang. Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence 2020, 1, 100002 .

AMA Style

Xieling Chen, Haoran Xie, Di Zou, Gwo-Jen Hwang. Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence. 2020; 1 ():100002.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Di Zou; Gwo-Jen Hwang. 2020. "Application and theory gaps during the rise of Artificial Intelligence in Education." Computers and Education: Artificial Intelligence 1, no. : 100002.

Journal article
Published: 22 March 2020 in Applied Sciences
Reads 0
Downloads 0

Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.

ACS Style

Xieling Chen; Haoran Xie; Gary Cheng; Leonard K. M. Poon; Mingming Leng; Fu Lee Wang. Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. Applied Sciences 2020, 10, 2157 .

AMA Style

Xieling Chen, Haoran Xie, Gary Cheng, Leonard K. M. Poon, Mingming Leng, Fu Lee Wang. Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. Applied Sciences. 2020; 10 (6):2157.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Gary Cheng; Leonard K. M. Poon; Mingming Leng; Fu Lee Wang. 2020. "Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis." Applied Sciences 10, no. 6: 2157.

Journal article
Published: 21 February 2020 in Computers & Education
Reads 0
Downloads 0

Computers & Education has been leading the field of computers in education for over 40 years, during which time it has developed into a well-known journal with significant influences on the educational technology research community. Questions such as “in what research topics were the academic community of Computers & Education interested?” “how did such research topics evolve over time?” and “what were the main research concerns of its major contributors?” are important to both the editorial board and readership of Computers & Education. To address these issues, this paper conducted a structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically. A structural topic model was used to profile the research hotspots. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas were identified. The major research concerns of the publications in Computers & Education by prolific countries/regions were shown and compared. Thus, this work provided useful insights and implications, and it could be used as a guide for contributors to Computers & Education.

ACS Style

Xieling Chen; Di Zou; Gary Cheng; Haoran Xie. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education 2020, 151, 103855 .

AMA Style

Xieling Chen, Di Zou, Gary Cheng, Haoran Xie. Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education. 2020; 151 ():103855.

Chicago/Turabian Style

Xieling Chen; Di Zou; Gary Cheng; Haoran Xie. 2020. "Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education." Computers & Education 151, no. : 103855.

Review
Published: 05 February 2020 in British Journal of Educational Technology
Reads 0
Downloads 0

The British Journal of Educational Technology (BJET) has been active in the field of educational technology since 1970. To celebrate its 50th anniversary and to demonstrate a comprehensive overview of the field, we conducted a bibliometric analysis of the 3710 publications in this journal from 1971 to 2018 as indexed in the Web of Science with full bibliographic information. This study aimed to (1) identify the publication and citation trends, (2) explore the distribution of paper types, (3) recognize the most relevant countries/regions, affiliations and authors, and (4) reveal relevant thematic features by analyzing publication abstracts and titles with the use of word cloud analysis and topic modeling analysis. The results highlighted several research hotspots and emerging topics such as Technology‐enhanced classroom pedagogy, Blended learning, Online social communities, Mobile assisted language learning, Game‐based learning and Socialized e‐learning.

ACS Style

Xieling Chen; Di Zou; Haoran Xie. Fifty years of British Journal of Educational Technology : A topic modeling based bibliometric perspective. British Journal of Educational Technology 2020, 51, 692 -708.

AMA Style

Xieling Chen, Di Zou, Haoran Xie. Fifty years of British Journal of Educational Technology : A topic modeling based bibliometric perspective. British Journal of Educational Technology. 2020; 51 (3):692-708.

Chicago/Turabian Style

Xieling Chen; Di Zou; Haoran Xie. 2020. "Fifty years of British Journal of Educational Technology : A topic modeling based bibliometric perspective." British Journal of Educational Technology 51, no. 3: 692-708.

Conference paper
Published: 10 November 2019 in Human Brain and Artificial Intelligence
Reads 0
Downloads 0

Artificial Intelligence (AI) plays an increasingly important role in advancing human brain research, given the continually growing number of academic research articles in the last decade. Meanwhile, human brain research can provide opportunities for the development of innovative AI techniques. Exploring and tracking patterns of the scientific articles of human brain research using AI can provide a comprehensive overview of the interdisciplinary field. Thus, this paper presents a bibliometric analysis to identify research status and development trend of the field between 2009 and 2018. Specifically, we analyze annual distributions of articles and their citations, identify prolific journals and affiliations, and visualize characteristics of scientific collaboration. Furthermore, research topics are analyzed and revealed. The obtained findings benefit scholars in the field, to understand the current status of research as well as monitoring scientific and technological activities.

ACS Style

Xieling Chen; Xinxin Zhang; Haoran Xie; Fu Lee Wang; Jun Yan; Tianyong Hao. Trends and Features of Human Brain Research Using Artificial Intelligence Techniques: A Bibliometric Approach. Human Brain and Artificial Intelligence 2019, 69 -83.

AMA Style

Xieling Chen, Xinxin Zhang, Haoran Xie, Fu Lee Wang, Jun Yan, Tianyong Hao. Trends and Features of Human Brain Research Using Artificial Intelligence Techniques: A Bibliometric Approach. Human Brain and Artificial Intelligence. 2019; ():69-83.

Chicago/Turabian Style

Xieling Chen; Xinxin Zhang; Haoran Xie; Fu Lee Wang; Jun Yan; Tianyong Hao. 2019. "Trends and Features of Human Brain Research Using Artificial Intelligence Techniques: A Bibliometric Approach." Human Brain and Artificial Intelligence , no. : 69-83.

Article
Published: 26 October 2019 in Journal of Computers in Education
Reads 0
Downloads 0

Targeted at analyzing the research status and trends of the educational technology field, this study conducted a bibliometric analysis on research topics, author profiles, and collaboration networks using a top-ranked journal Computers & Education (ISSN: 0360-1315). Using the Web of Sciences database, we retrieved 3963 articles published by the journal during the period 1978–2018. The annual distribution of articles demonstrates a significant increase in the journal publications, especially from 2005 to 2011. The scientific collaboration between authors, institutions, and countries/regions has become increasingly close. The scientific collaboration rate between authors from the same institution, and from the same country/region, is relatively higher compared with those from different institutions and countries/regions. Keyword evolution analysis highlights some prevalent topics such as “interactive learning environment,” “teaching/learning strategies,” “pedagogical issue,” and “improving classroom teaching.” Findings of this study provide a comprehensive overview of the articles on educational technology over the past 40 years.

ACS Style

Xieling Chen; Guoxing Yu; Gary Cheng; Tianyong Hao. Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: a bibliometric analysis. Journal of Computers in Education 2019, 6, 563 -585.

AMA Style

Xieling Chen, Guoxing Yu, Gary Cheng, Tianyong Hao. Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: a bibliometric analysis. Journal of Computers in Education. 2019; 6 (4):563-585.

Chicago/Turabian Style

Xieling Chen; Guoxing Yu; Gary Cheng; Tianyong Hao. 2019. "Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: a bibliometric analysis." Journal of Computers in Education 6, no. 4: 563-585.

Journal article
Published: 11 April 2019 in Computers & Education
Reads 0
Downloads 0

Classroom dialogue is a commonly used method for teaching and learning, and a close study of dialogue has increasingly become an active field of research. In order to have a comprehensive overview of the field, a bibliometric analysis was conducted on 3914 papers published from 1999 to 2018 retrieved from WoS database in relation to classroom dialogue. Specifically, we analyzed trends in publications and citations, recognized prolific authors, institutions and journals, identified geographical publication distributions, visualized the characteristics of collaboration among authors, institutions, and countries/regions, as well as revealing the evolution of themes over the past 20 years. Findings include, firstly, the fact that publications and citations in relation to classroom dialogue have grown consistently over the past 20 years. Secondly, the USA has contributed dramatically more publications, especially since the year 2012. Thirdly, scientific collaborations in perspectives of country/region, institution and author can be explored by accessing the dynamic social networks. Fourthly, thematic features in relation to research on classroom dialogue were revealed by analyzing keywords, with several recurring keywords being identified throughout the period (e.g. ‘classroom’, ‘discourse’, ‘student’) and at the same time, new keywords have emerged (e.g. ‘technology’, ‘computer-mediated communication’), which reflect the shifting trends in the field. This work is useful in terms of indicating the current status of research to scholars as well as practitioners, enabling them to be more aware of the research hotspots when making decisions about which topic to address.

ACS Style

Yu Song; Xieling Chen; Tianyong Hao; Zhinan Liu; Zixin Lan. Exploring two decades of research on classroom dialogue by using bibliometric analysis. Computers & Education 2019, 137, 12 -31.

AMA Style

Yu Song, Xieling Chen, Tianyong Hao, Zhinan Liu, Zixin Lan. Exploring two decades of research on classroom dialogue by using bibliometric analysis. Computers & Education. 2019; 137 ():12-31.

Chicago/Turabian Style

Yu Song; Xieling Chen; Tianyong Hao; Zhinan Liu; Zixin Lan. 2019. "Exploring two decades of research on classroom dialogue by using bibliometric analysis." Computers & Education 137, no. : 12-31.

Journal article
Published: 09 April 2019 in BMC Medical Informatics and Decision Making
Reads 0
Downloads 0

Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field. With the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis. Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected. This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.

ACS Style

Xieling Chen; Yonghui Lun; Jun Yan; Tianyong Hao; Heng Weng. Discovering thematic change and evolution of utilizing social media for healthcare research. BMC Medical Informatics and Decision Making 2019, 19, 50 .

AMA Style

Xieling Chen, Yonghui Lun, Jun Yan, Tianyong Hao, Heng Weng. Discovering thematic change and evolution of utilizing social media for healthcare research. BMC Medical Informatics and Decision Making. 2019; 19 (2):50.

Chicago/Turabian Style

Xieling Chen; Yonghui Lun; Jun Yan; Tianyong Hao; Heng Weng. 2019. "Discovering thematic change and evolution of utilizing social media for healthcare research." BMC Medical Informatics and Decision Making 19, no. 2: 50.

Journal article
Published: 11 February 2019 in Online Information Review
Reads 0
Downloads 0

The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications. First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results. The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered. This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.

ACS Style

Xieling Chen; Shan Wang; Yong Tang; Tianyong Hao. A bibliometric analysis of event detection in social media. Online Information Review 2019, 43, 29 -52.

AMA Style

Xieling Chen, Shan Wang, Yong Tang, Tianyong Hao. A bibliometric analysis of event detection in social media. Online Information Review. 2019; 43 (1):29-52.

Chicago/Turabian Style

Xieling Chen; Shan Wang; Yong Tang; Tianyong Hao. 2019. "A bibliometric analysis of event detection in social media." Online Information Review 43, no. 1: 29-52.

Comparative study
Published: 07 December 2018 in BMC Medical Informatics and Decision Making
Reads 0
Downloads 0

The application of artificial intelligence techniques for processing electronic health records data plays increasingly significant role in advancing clinical decision support. This study conducts a quantitative comparison on the research of utilizing artificial intelligence on electronic health records between the USA and China to discovery their research similarities and differences. Publications from both Web of Science and PubMed are retrieved to explore the research status and academic performances of the two countries quantitatively. Bibliometrics, geographic visualization, collaboration degree calculation, social network analysis, latent dirichlet allocation, and affinity propagation clustering are applied to analyze research quantity, collaboration relations, and hot research topics. There are 1031 publications from the USA and 173 publications from China during 2008-2017 period. The annual numbers of publications from the USA and China increase polynomially. JAMIA with 135 publications and JBI with 13 publications are the top prolific journals for the USA and China, respectively. Harvard University with 101 publications and Zhejiang University with 12 publications are the top prolific affiliations for the USA and China, respectively. Massachusetts is the most prolific region with 211 publications for the USA, while for China, Taiwan is the top 1 with 47 publications. China has relatively higher institutional and international collaborations. Nine main research areas for the USA are identified, differentiating 7 for China. There is a steadily growing presence and increasing visibility of utilizing artificial intelligence on electronic health records for the USA and China over the years. The results of the study demonstrate the research similarities and differences, as well as strengths and weaknesses of the two countries.

ACS Style

Xieling Chen; Ziqing Liu; Li Wei; Jun Yan; Tianyong Hao; Ruoyao Ding. A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017. BMC Medical Informatics and Decision Making 2018, 18, 117 .

AMA Style

Xieling Chen, Ziqing Liu, Li Wei, Jun Yan, Tianyong Hao, Ruoyao Ding. A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017. BMC Medical Informatics and Decision Making. 2018; 18 (5):117.

Chicago/Turabian Style

Xieling Chen; Ziqing Liu; Li Wei; Jun Yan; Tianyong Hao; Ruoyao Ding. 2018. "A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017." BMC Medical Informatics and Decision Making 18, no. 5: 117.

Focus
Published: 06 September 2018 in Soft Computing
Reads 0
Downloads 0

Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.

ACS Style

Tianyong Hao; Xieling Chen; Guozheng Li; Jun Yan. A bibliometric analysis of text mining in medical research. Soft Computing 2018, 22, 7875 -7892.

AMA Style

Tianyong Hao, Xieling Chen, Guozheng Li, Jun Yan. A bibliometric analysis of text mining in medical research. Soft Computing. 2018; 22 (23):7875-7892.

Chicago/Turabian Style

Tianyong Hao; Xieling Chen; Guozheng Li; Jun Yan. 2018. "A bibliometric analysis of text mining in medical research." Soft Computing 22, no. 23: 7875-7892.

Review
Published: 28 June 2018 in Wireless Communications and Mobile Computing
Reads 0
Downloads 0

Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.

ACS Style

Xieling Chen; Ruoyao Ding; Kai Xu; Shan Wang; Tianyong Hao; Yi Zhou. A Bibliometric Review of Natural Language Processing Empowered Mobile Computing. Wireless Communications and Mobile Computing 2018, 2018, 1 -21.

AMA Style

Xieling Chen, Ruoyao Ding, Kai Xu, Shan Wang, Tianyong Hao, Yi Zhou. A Bibliometric Review of Natural Language Processing Empowered Mobile Computing. Wireless Communications and Mobile Computing. 2018; 2018 ():1-21.

Chicago/Turabian Style

Xieling Chen; Ruoyao Ding; Kai Xu; Shan Wang; Tianyong Hao; Yi Zhou. 2018. "A Bibliometric Review of Natural Language Processing Empowered Mobile Computing." Wireless Communications and Mobile Computing 2018, no. : 1-21.

Journal article
Published: 22 March 2018 in BMC Medical Informatics and Decision Making
Reads 0
Downloads 0

Natural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field. We conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007–2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method. There were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country’s publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc. A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.

ACS Style

Xieling Chen; Haoran Xie; Fu Lee Wang; Ziqing Liu; Juan Xu; Tianyong Hao. A bibliometric analysis of natural language processing in medical research. BMC Medical Informatics and Decision Making 2018, 18, 1 -14.

AMA Style

Xieling Chen, Haoran Xie, Fu Lee Wang, Ziqing Liu, Juan Xu, Tianyong Hao. A bibliometric analysis of natural language processing in medical research. BMC Medical Informatics and Decision Making. 2018; 18 (1):1-14.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Fu Lee Wang; Ziqing Liu; Juan Xu; Tianyong Hao. 2018. "A bibliometric analysis of natural language processing in medical research." BMC Medical Informatics and Decision Making 18, no. 1: 1-14.