Search (316 results, page 16 of 16)

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Abstract

In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

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Abstract

The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.

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Abstract

Using the option Analyze Results with the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Sciences Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy" (Izsák & Papp, 1995). Since this indicator of interdisciplinarity is normalized between 0 and 1, interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel, Guillaume, Lambiotte, and Lefebvre's (2008) community-finding algorithms operating on the relations among journals included in the JCR. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: Local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.

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Abstract

This study continues a long history of author cocitation analysis (and more recently, author bibliographic coupling analysis) of the intellectual structure of information science (IS) into the time period 2006 to 2010 (IS 2006-2010). We find that web technologies continue to drive developments, especially at the research front, although perhaps more indirectly than before. A broadening of perspectives is visible in IS 2006-2010, where network science becomes influential and where full-text analysis methods complement traditional computer science influences. Research in the areas of the h-index and mapping of science appears to have been highlights of IS 2006-2011. This study tests and confirms a forecast made previously by comparing knowledge-base and research-front findings for IS 2001-2005, which expected both the information retrieval (IR) systems and webometrics specialties to shrink in 2006 to 2010. A corresponding comparison of the knowledge base and research front of IS 2006-2010 suggests a continuing decline of the IR systems specialty in the near future, but also a considerable (re)growth of the webometrics area after a period of decline from 2001 to 2005 and 2006 to 2010, with the latter due perhaps in part to its contribution to an emerging web science.

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Abstract

The organization of large collections of documents has become more important with the increase in the amount of digital information available. In certain constricted domains of knowledge, keywords and subject descriptors tend to be similar and therefore insufficient to differentiate documents. In this context, instead of relying only on the presence of common terms, the identification of common cited references can be useful to define semantic relationship among documents. The purpose of this work is to add another instance on the research linking information retrieval and bibliometric techniques aided by information technology. A domain analytic method was developed to generate clusters of documents, which uses self-organizing maps, in the scope of artificial neural networks, to categorize documents. The results obtained show that this approach successfully identified clusters of authors and documents through their cited references. In addition, further qualitative analysis of these clusters demonstrates the existence of semantic relationships between the documents. This study can contribute to the development of the field of knowledge organization by evaluating the use of artificial neural networks in the automatic categorization of documents in a constricted knowledge domain based on the analysis of the references cited by these documents.

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Abstract

The citation relationships between publications, which are significant for assessing the importance of scholarly components within a network, have been used for various scientific applications. Missing citation metadata in scholarly databases, however, create problems for classical citation-based ranking algorithms and challenge the performance of citation-based retrieval systems. In this research, we utilize a two-step citation analysis method to investigate the importance of publications for which citation information is partially missing. First, we calculate the importance of the author and then use his importance to estimate the publication importance for some selected articles. To evaluate this method, we designed a simulation experiment-"random citation-missing"-to test the two-step citation analysis that we carried out with the Association for Computing Machinery (ACM) Digital Library (DL). In this experiment, we simulated different scenarios in a large-scale scientific digital library, from high-quality citation data, to very poor quality data, The results show that a two-step citation analysis can effectively uncover the importance of publications in different situations. More importantly, we found that the optimized impact from the importance of an author (first step) is exponentially increased when the quality of citation decreases. The findings from this study can further enhance citation-based publication-ranking algorithms for real-world applications.

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Abstract

Interests of researchers who engage with research synthesis methods (RSM) intersect with library and information science (LIS) research and practice. This intersection is described by a summary of conceptualizations of research synthesis in a diverse set of research fields and in the context of Swanson's (1986) discussion of undiscovered public knowledge. Through a selective literature review, research topics that intersect with LIS and RSM are outlined. Topics identified include open access, information retrieval, bias and research information ethics, referencing practices, citation patterns, and data science. Subsequently, bibliometrics and topic modeling are used to present a systematic overview of the visibility of RSM in LIS. This analysis indicates that RSM became visible in LIS in the 1980s. Overall, LIS research has drawn substantially from general and internal medicine, the field's own literature, and business; and is drawn on by health and medical sciences, computing, and business. Through this analytical overview, it is confirmed that research synthesis is more visible in the health and medical literature in LIS; but suggests that, LIS, as a meta-science, has the potential to make substantive contributions to a broader variety of fields in the context of topics related to research synthesis methods.

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Series

Synthesis lectures on information concepts, retrieval, and services; 52

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Abstract

Purpose This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015. Design/methodology/approach The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology. Findings The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed. Originality/value Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

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Date

20. 1.2015 18:30:22

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Abstract

Many authors have posited a social component in citation, the consensus being that the citers and citees often have interpersonal as well as intellectual ties. Evidence for this belief has been rather meager, however, in part because social networks researchers have lacked bibliometric data (e.g., pairwise citation counts from online databases), and citation analysts have lacked sociometric data (e.g., pairwise measures of acquaintanceship). In 1997 Nazer extensively measured personal relationships and communication behaviors in what we call "Globenet," an international group of 16 researchers from seven disciplines that was established in 1993 to study human development. Since Globenet's membership is known, it was possible during 2002 to obtain citation records for all members in databases of the Institute for Scientific Information. This permitted examination of how members cited each other (intercited) in journal articles over the past three decades and in a 1999 book to which they all contributed. It was also possible to explore links between the intercitation data and the social and communication data. Using network-analytic techniques, we look at the growth of intercitation over time, the extent to which it follows disciplinary or interdisciplinary lines, whether it covaries with degrees of acquaintanceship, whether it reflects Globenet's organizational structure, whether it is associated with particular in-group communication patterns, and whether it is related to the cocitation of Globenet members. Results show cocitation to be a powerful predictor of intercitation in the journal articles, while being an editor or co-author is an important predictor in the book. Intellectual ties based an shared content did better as predictors than content-neutral social ties like friendship. However, interciters in Globenet communicated more than did noninterciters.

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Footnote

Rez. in: JASIST 55(2004) no.4, S.363-365 (J.W. Schneider): "Theories and methods for mapping scientific frontiers have existed for decades-especially within quantitative studies of science. This book investigates mapping scientific frontiers from the perspective of visual thinking and visual exploration (visual communication). The central theme is construction of visual-spatial representations that may convey insights into the dynamic structure of scientific frontiers. The author's previous book, Information Visualisation and Virtual Environments (1999), also concerns some of the ideas behind and possible benefits of visual communication. This new book takes a special focus an knowledge visualization, particularly in relation to science literature. The book is not a technical tutorial as the focus is an principles of visual communication and ways that may reveal the dynamics of scientific frontiers. The new approach to science mapping presented is the culmination of different approaches from several disciplines, such as philosophy of science, information retrieval, scientometrics, domain analysis, and information visualization. The book therefore addresses an audience with different disciplinary backgrounds and tries to stimulate interdisciplinary research. Chapter 1, The Growth of Scientific Knowledge, introduces a range of examples that illustrate fundamental issues concerning visual communication in general and science mapping in particular. Chapter 2, Mapping the Universe, focuses an the basic principles of cartography for visual communication. Chapter 3, Mapping the Mind, turns the attention inward and explores the design of mind maps, maps that represent our thoughts, experience, and knowledge. Chapter 4, Enabling Techniques for Science Mapping, essentially outlines the author's basic approach to science mapping.

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Abstract

According to Jean M. Tague-Sutcliffe "informetrics" is "the study of the quantitative aspects of information in any form, not just records or bibliographies, and in any social group, not just scientists" (Tague-Sutcliffe, 1992, 1). Leo Egghe also defines "informetrics" in a very broad sense. "(W)e will use the term' informetrics' as the broad term comprising all-metrics studies related to information science, including bibliometrics (bibliographies, libraries,...), scientometrics (science policy, citation analysis, research evaluation,...), webometrics (metrics of the web, the Internet or other social networks such as citation or collaboration networks), ..." (Egghe, 2005b,1311). According to Concepcion S. Wilson "informetrics" is "the quantitative study of collections of moderatesized units of potentially informative text, directed to the scientific understanding of information processes at the social level" (Wilson, 1999, 211). We should add to Wilson's units of text also digital collections of images, videos, spoken documents and music. Dietmar Wolfram divides "informetrics" into two aspects, "system-based characteristics that arise from the documentary content of IR systems and how they are indexed, and usage-based characteristics that arise how users interact with system content and the system interfaces that provide access to the content" (Wolfram, 2003, 6). We would like to follow Tague-Sutcliffe, Egghe, Wilson and Wolfram (and others, for example Björneborn & Ingwersen, 2004) and call this broad research of empirical information science "informetrics". Informetrics includes therefore all quantitative studies in information science. If a scientist performs scientific investigations empirically, e.g. on information users' behavior, on scientific impact of academic journals, on the development of the patent application activity of a company, on links of Web pages, on the temporal distribution of blog postings discussing a given topic, on availability, recall and precision of retrieval systems, on usability of Web sites, and so on, he or she contributes to informetrics. We see three subject areas in information science in which such quantitative research takes place, - information users and information usage, - evaluation of information systems, - information itself, Following Wolfram's article, we divide his system-based characteristics into the "information itself "-category and the "information system"-category. Figure 1 is a simplistic graph of subjects and research areas of informetrics as an empirical information science.

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Abstract

"Thomson Reuters' citation indexes i.e. SCI, SSCI and AHCI are said to be "authoritative". Due to the huge influence of these databases on global academic evaluation of productivity and impact, Terje Tüür-Fröhlich decided to conduct case studies on the data quality of Social Sciences Citation Index (SSCI) records. Tüür-Fröhlich investigated articles from social science and law. The main findings: SSCI records contain tremendous amounts of "trivial errors", not only misspellings and typos as previously mentioned in bibliometrics and scientometrics literature. But Tüür-Fröhlich's research documented fatal errors which have not been mentioned in the scientometrics literature yet at all. Tüür-Fröhlich found more than 80 fatal mutations and mutilations of Pierre Bourdieu (e.g. "Atkinson" or "Pierre, B. and "Pierri, B."). SSCI even generated zombie references (phantom authors and works) by data fields' confusion - a deadly sin for a database producer - as fragments of Patent Laws were indexed as fictional author surnames/initials. Additionally, horrific OCR-errors (e.g. "nuxure" instead of "Nature" as journal title) were identified. Tüür-Fröhlich´s extensive quantitative case study of an article of the Harvard Law Review resulted in a devastating finding: only 1% of all correct references from the original article were indexed by SSCI without any mistake or error. Many scientific communication experts and database providers' believe that errors in databanks are of less importance: There are many errors, yes - but they would counterbalance each other, errors would not result in citation losses and would not bear any effect on retrieval and evaluation outcomes. Terje Tüür-Fröhlich claims the contrary: errors and inconsistencies are not evenly distributed but linked with languages biases and publication cultures."

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Abstract

Purpose Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data. Design/methodology/approach We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics. Findings Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not. Originality/value Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.

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Abstract

Purpose This study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011-2020. It also examines changes in this structure from 2006-2010 through 2011-2015 to 2016-2020. Results will contribute to a better understanding of the information science research field. Design/methodology/approach The well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011-2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor. Findings The two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011-2015 but shared dominance with information behavior during 2016-2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016-2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011-2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016-2020. Originality/value Author co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.