Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014

Olmeda-Gómez, Carlos; Ovalle-Perandones, María-Antonia; Perianes‐Rodríguez, Antonio

doi:10.1007/s11192-017-2486-8

Cited by 50 publications

(20 citation statements)

References 71 publications

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“…Clustering analysis refers to the clustering of some keywords with strong cooccurrence intensity. Co-words analysis is mainly used in the fields of artificial intelligence (Courtial and Law 1989;Bullinaria and Levy 2007), scientific metrology (Lamnabhilagarrigue et al 2017;Olmedagomez et al 2017), materials science (Linnenluecke et al 2017), medical science (Buchwald et al 2004;Chowdhury et al 2014), and economics (Topalli and Ivanaj 2016;Bracco et al 2018).…”

Section: Social Network Analysis Methodsmentioning

confidence: 99%

Mapping the knowledge of green consumption: a meta-analysis

Zhao

Geng

Sun

et al. 2020

Environ Sci Pollut Res

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Green consumption can facilitate sustainable industrial development and improve the overall efficiency of resource utilization. In response to rapid economic development and increasing environmental emissions, it is critical to promote green consumption so that the whole society can move toward sustainable development. This study aims to systematically review studies on green consumption by means of meta-analysis, bibliometric analysis, and social network analysis. The results show that green consumption is an interdisciplinary research field, involving environmental science, social science, medical science, economics, and other disciplines. Most productive countries, institutions, authors are identified so that the new researchers in this field can find their research partners. Keywords analysis results help identify the research hotpots in this field. It is suggested that future green consumption research should focus on behavior mechanism, stakeholder coordination, and policy evaluation. In general, the results obtained from this study provide valuable information for researchers and practitioners to promote green consumption research.

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Section: Social Network Analysis Methodsmentioning

confidence: 99%

Mapping the knowledge of green consumption: a meta-analysis

Zhao

Geng

Sun

et al. 2020

Environ Sci Pollut Res

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“…Given a network of keywords, a compound of community detection, network analysis, and strategic diagrams are depicted to detect interesting trends and patterns within a complex keyword interaction network and reveal the conceptual structure of a research field (Callon et al, 1991 ). The method has been widely utilized in various areas like urbanization (Zhang et al, 2017 ), supply chain management (Shiau et al, 2015 ), library and information science (Olmeda-Gómez et al, 2017 ), the Internet of Things (Yan et al, 2015 ), marketing (Z. Wang et al, 2015 ), social media (Gan & Wang, 2015 ), scientometrics (Ravikumar et al, 2015 ), non-biomedical modalities (Nguyen, 2019 ), medical tourism research (Hoz-Correa et al, 2018 ), child-computer interaction (Giannakos et al, 2020 ), energy (Wu & Leu, 2014 ), open data (Corrales-Garay et al, 2019 ), recommendation systems (Hu & Zhang, 2015 ), gender differences (Dehdarirad et al, 2014 ), and epidemiology (Baziyad et al, 2020 ). Some papers have also utilized co-word analysis in coronavirus research areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Mapping the intellectual structure of the coronavirus field (2000–2020): a co-word analysis

et al. 2021

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Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain’s structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) “Antibody-Virus Interactions,” (b) “Emerging Infectious Diseases,” (c) “Protein Structure-based Drug Design and Antiviral Drug Discovery,” (d) “Coronavirus Detection Methods,” (e) “Viral Pathogenesis and Immunity,” and (f) “Animal Coronaviruses.” The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, “Antibody-virus Interactions,” “Emerging Infectious Diseases,” and “Coronavirus Detection Methods” hold interesting, promising research gaps to be both explored and filled in the future.

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“…In China, there are studies for foreign language papers [2] [3] [4], Chinese journal papers [5] [6] [7], and studies for the National Social Science Foundation projects [8] [9]. The identification of research themes in the field of library intelligence has been carried out earlier abroad, and more types of hot research analysis methods are used: content analysis [10], LDA theme analysis [11] [12], co-word analysis [13] [14] [15], and so on. The advantage of content analysis is that the findings are objective and not influenced by the subjective attitude of the researcher, but the disadvantage is that this method can only analyze the explicit communication content, and the intrinsic validity is not high; the advantage of LDA thematic analysis is that it has a clearer purpose and can better reflect the differences between samples, but the disadvantage is that this method is not suitable for short text analysis, and the model effect is not good.…”

Section: Review Of the Current Status Of Domestic And International Researchmentioning

confidence: 99%

Analysis of Research Directions in the Field of Library Intelligence from the Perspective of Altmetrics

Li¹,

Xia²

2021

IIM

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With the continuous development of science and technology, the academic communication of many scholars slowly shifts to the web, and people's academic communication in social media gradually increases. Traditional metrics have a certain one-sidedness in the evaluation of disciplinary literature and cannot evaluate the impact of literature on the web; while Altmetrics can count how literature is followed and utilized on the web, which is a complement to the traditional measurement methods. Therefore, it is necessary to evaluate the impact of academic literature based on Altmetrics, and combining it with traditional citation indicators can strengthen the credibility and objectivity of academic evaluation and promote better dissemination and communication of academic results. Against this research background, this paper combed through the most influential articles in the field of library and information science in recent years, analysed the literature on library intelligence in the WOS database, identified current research hotspots in the field of library and information science, and predicted potential research hotspots in the future. This study may provide a reference for the research and development of the discipline.

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Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014

Cited by 50 publications

References 71 publications

Mapping the knowledge of green consumption: a meta-analysis

Mapping the knowledge of green consumption: a meta-analysis

Mapping the intellectual structure of the coronavirus field (2000–2020): a co-word analysis

Analysis of Research Directions in the Field of Library Intelligence from the Perspective of Altmetrics

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