Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Hu, B. ; Dong, X. ; Zhang, C. ; Bowman, T.D. ; Ding, Y. ; Milojevic, S. ; Ni, C. ; Yan, E. ; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications.
In: Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656.
Abstract: This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23347/abstract.
Themenfeld: Elektronisches Publizieren
2Zhang, G. ; Ding, Y. ; Milojevic, S.: Citation content analysis (CCA) : a framework for syntactic and semantic analysis of citation content.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1490-1503.
Abstract: This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.
Themenfeld: Citation indexing
3Milojevic, S. ; Sugimoto, C.R. ; Yan, E. ; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953.
Abstract: This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.
Wissenschaftsfach: Bibliothekswesen ; Informationswissenschaft
4Milojevic, S.: Modes of collaboration in modern science : beyond power laws and preferential attachment.
In: Journal of the American Society for Information Science and Technology. 61(2010) no.7, S.1410-1423.
Abstract: The goal of the study was to determine the underlying processes leading to the observed collaborator distribution in modern scientific fields, with special attention to nonpower-law behavior. Nanoscience is used as a case study of a modern interdisciplinary field and its coauthorship network for 2000-2004 period is constructed from the NanoBank database. We find three collaboration modes that correspond to three distinct ranges in the distribution of collaborators: (1) for authors with fewer than 20 collaborators (the majority) preferential attachment does not hold and they form a log-normal hook instead of a power law; (2) authors with more than 20 collaborators benefit from preferential attachment and form a power law tail; and (3) authors with between 250 and 800 collaborators are more frequent than expected because of the hyperauthorship practices in certain subfields.
5Milojevic, S.: Power law distributions in information science : making the case for logarithmic binning.
In: Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2417-2425.
Abstract: We suggest partial logarithmic binning as the method of choice for uncovering the nature of many distributions encountered in information science (IS). Logarithmic binning retrieves information and trends "not visible" in noisy power law tails. We also argue that obtaining the exponent from logarithmically binned data using a simple least square method is in some cases warranted in addition to methods such as the maximum likelihood. We also show why often-used cumulative distributions can make it difficult to distinguish noise from genuine features and to obtain an accurate power law exponent of the underlying distribution. The treatment is nontechnical, aimed at IS researchers with little or no background in mathematics.