Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses

Chang, Yu-Wei; Huang, Mu‐Hsuan; Lin, Chiao‐Wen

doi:10.1007/s11192-015-1762-8

Cited by 235 publications

(150 citation statements)

References 21 publications

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“…Further analysis for mapping the specific research area is based on author keywords that appeared in at least two papers. The usage of keywords is accepted by the literature and successfully applied also to other research areas (Su and Lee, 2010;Yoon et al, 2010;Chang et al, 2015;Khan and Wood, 2015). After this, each selected paper is given a binary value (0, 1) for each descriptor.…”

Section: Methodological Notes About Homals Analysismentioning

confidence: 99%

“…For example, if a journal is in the first quadrant it does not necessarily have the same keywords shown in the first quadrant of keyword's proximity map. According to the literature (Su and Lee, 2010;Yoon et al, 2010;Chang et al, 2015;Khan and Wood, 2015), the keywords included in our analysis are summarized in Table 4. In the journal positioning map we include the most repeated words given by authors with a total number of fifteen or more appearances.…”

Section: Homals Abstract Positioningmentioning

confidence: 99%

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Product and process innovation in manufacturing firms: a 30-year bibliometric analysis

et al. 2017

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Section: Methodological Notes About Homals Analysismentioning

confidence: 99%

Section: Homals Abstract Positioningmentioning

confidence: 99%

Product and process innovation in manufacturing firms: a 30-year bibliometric analysis

et al. 2017

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“…Co-citation networks, bibliographic coupling and direct citations are some of the most significant bibliometric networks we can use to map citations from Wikipedia entries; of these, co-citation networks are the most popular in research [30,31]. If we take into account other types of network such as co-author and co-word, the aforementioned three methods show a high degree of similarity [32].…”

Section: Analysis Of Co-citation Networkmentioning

confidence: 99%

Science through Wikipedia: A novel representation of open knowledge through co-citation networks

Arroyo-Machado¹,

Torres‐Salinas²,

Herrera‐Viedma³

et al. 2020

PLoS ONE

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This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals.

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“…However, in the last years there have appeared different studies that try to establish the patterns of the AK over time. These studies use methods mainly based on the frequency of the occurrence of terms over the years, employing in some cases sophisticated methods of normalization, bibliometric indicators and linear regression models (Chang, Huang, & Lin, 2015;Chen & Xiao, 2016;Faust, 2018;M. Wang & Chai, 2018;Xu et al, 2018).…”

Section: Survival Of Author Abstractmentioning

confidence: 99%

Survival analysis of author keywords: An application to the library and information sciences area

Peset

Garzón-Farinós

Moreno

et al. 2019

Asso for Info Science & Tech

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Our purpose is to adapt a statistical method for the analysis of discrete numerical series to the keywords appearing in scientific articles of a given area. As an example, we apply our methodological approach to the study of the keywords in the Library and Information Sciences (LIS) area. Our objective is to detect the new author keywords that appear in a fixed knowledge area in the period of 1 year in order to quantify the probabilities of survival for 10 years as a function of the impact of the journals where they appeared. Many of the new keywords appearing in the LIS field are ephemeral. Actually, more than half are never used again. In general, the terms most commonly used in the LIS area come from other areas. The average survival time of these keywords is approximately 3 years, being slightly higher in the case of words that were published in journals classified in the second quartile of the area. We believe that measuring the appearance and disappearance of terms will allow understanding some relevant aspects of the evolution of a discipline, providing in this way a new bibliometric approach.

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Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses

Cited by 235 publications

References 21 publications

Product and process innovation in manufacturing firms: a 30-year bibliometric analysis

Product and process innovation in manufacturing firms: a 30-year bibliometric analysis

Science through Wikipedia: A novel representation of open knowledge through co-citation networks

Survival analysis of author keywords: An application to the library and information sciences area

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