Information sciences 1968–2016: A retrospective analysis with text mining and bibliometric
Introduction
Information Sciences (INS) is one of the most influential journals in the field of computer science. According to its official website, it has a high impact factor and enjoys wide readership. The 2015 Journal Citation Reports indicate that INS ranked 8th out of 144 ‘computer science, information sciences’ - categorized journals and gained an impact factor of 3.364. Since INS started in 1968, it has made important contributions to the overall development of the discipline of information science. Nearing its 50th anniversary, it is of interest to complete a retrospective analysis of the INS publications.
Bibliometric techniques [82] are the widely used methods by scholars for analyzing a particular journal to provide an overview of the research trends encountered in that journal. When the publications of a single journal are analyzed based on bibliometric techniques, this generates a broad picture of the journal, offering an inner structure pattern [67], [83]. It can reveal the quality, thematic and citation landscape of the journal in any field. This type of studies appeared about three decades ago. Heck and Bremser [24] analyzed the author and institutional contributors of the Accounting Review and discovered the development paths of this journal. Naqvi [47] completed a comprehensive review on Journal of Documentation based on bibliometric techniques and 251 publications appeared on this journal between 1994 and 2003. Some important conclusions were obtained such as the co-authorship and the annual distribution of its publications. Van Fleet et al. [66] completed a brief overview of the Journal of Management, which is one of the flagship journals in the field of management. The journal's possible future was also identified in this study. Chen et al. [14] studied the inner structure, topic clusters and citation landscapes of Data & Knowledge Engineering publications (1985–2007). A series of thematic trends were identified through co-citation analysis and citation burst detection. Chan et al. [10] presented a retrospective analysis of the publications of European Financial Management from 1995 to 2008. Borokhovich et al. [7] introduced a citation-based method for evaluating the journal's influence, a case study about the Journal of Banking and Finance was presented to illustrate the proposed method. In commemoration of the anniversary 25th of Knowledge-Based System, Cobo et al. [15] made a bibliometric analysis of the publications of the journal between 1991 and 2014. Two emerging research topics such as ontology and social networks were clearly identified. Calma and Davies [9] proposed a citation network-based approach for analyzing all publications of Studies in Higher Education from 1976 to 2013. In this study, the most cited keywords, productive authors and cited authors were revealed. Merigó et al. [44] presented a comprehensive overview of the publications of the Journal of Business Research by using bibliometric techniques. The main factors that affect the development of this journal have been explored. The publications of Academy of Management Journal from 1958 to 2014 were investigated and discussed in detail by Calma and Davies [8]. The most influential contributors, most cited articles, most productive institutions and countries responsible for the publications were also identified.
The above research results show that the bibliometric analysis of a single journal has received great attention from scholars all over the world and a series of remarkable achievements have also been made across various disciplines. The reason behind this phenomenon is that this type of analysis can form a general picture of the corresponding journal, offer an accurate description that provides an insight that is beyond the superficial [67]. INS is an important and significant journal in the field of computer science. Bibliometric analysis on the publications of INS is very important and indispensable since it could provide useful information for all the stakeholders of INS.
The aim of this study is to reveal the inner structure pattern and citation landscape of INS publications from a general perspective. Conventional bibliometric methods and customized text mining techniques were performed by analyzing annual publication outputs, publications distribution by country and institution, the authorship productivity and collaboration pattern to provide an alternate perspective on the evolvement and development of INS. Moreover, the innovative approach such as document co-citation network analysis, research clusters identification and analysis, and references citation bursts detection are performed to offer insights into the research topics and trends evaluation over time from different perspectives, which is helpful for future research.
Section snippets
Characteristics of article outputs
The data source for this study is 7721 INS articles found in the Web of Science dating between 1968 and 2016 (until September 20, 2016; to be more precise) and the contained 153,606 references.
Bibliographic landscape
In this section, we mainly focus on the visualization of INS publications with the help of CiteSpace which is a freely available Java application designed for knowledge domain visualization [11], [39], [62]. This section mainly studies the networks of document co-citation and the keywords co-occurring for exploring the knowledge structure and development trends of INS.
Conclusions
In this study, we have provided a comprehensive perspective at the evolution and development of INS. Based on the use of the bibliometric methods and customized text mining techniques, we have analyzed the patterns of INS publications, geographic distribution, source journals, source institutes, international collaboration, inter-institutional collaboration, document co-citation network and the references citation bursts detection.
INS itself, Lecture Notes in Computer Science, Fuzzy Sets and
Acknowledgements
The work was supported in part by the China National Natural Science Foundation under Grant 71571123, in part by the Zhejiang Science & Technology Plan of China under Grant 2015C33024, and in part by Zhejiang Provincial Natural Science Foundation of China under Grant LY17G010007.
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