Visualization of co-readership patterns from an online reference management system

Kraker, Peter; Schlögl, Christian; Jack, Kris; Lindstaedt, Stefanie

doi:10.1016/j.joi.2014.12.003

Cited by 20 publications

(22 citation statements)

References 41 publications

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“…These are the least developed and more research will be necessary to fully grasp the possibilities of these analyses. In this section we will just focus on three basic examples of current applications: the analysis of communities of attention (Haustein, Bowman, & Costas, 2015a), hashtag coupling analysis (van Honk & Costas, 2016) and reading/reader pattern analysis (Haunschild, Bornmann, & Leydesdorff, 2015;Kraker, Schlögl, Jack, & Lindstaedt, 2015;Zahedi & Van Eck, 2014).…”

Section: Network-based Indicatorsmentioning

confidence: 99%

“…Reading/reader pattern analysis Data extracted from reference manager tools such as Mendeley or CiteULike has been used for knowledge domain detection purpose or for finding common interests among their users (Kraker, et al, 2015;Jiang, He, & Ni, 2011). The idea is similar to co-citation (Boyack & Klavans, 2010;Small, 1973).…”

Section: Hashtag Coupling Analysismentioning

confidence: 99%

“…The idea is similar to co-citation (Boyack & Klavans, 2010;Small, 1973). Those publications with high co-occurrence in different users' profiles are considered to be more similar in terms of their thematic subject (Kraker, et al, 2015). The network of user groups in Mendeley saving the same set of publications showed that students and postdocs have more common topical interests than other user groups (Haunschild, Bornmann, & Leydesdorff, 2015).…”

Section: Hashtag Coupling Analysismentioning

confidence: 99%

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Social Media Metrics for New Research Evaluation

Wouters

Zahedi

Costas

2019

Springer Handbook of Science and Technology Indicators

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This chapter approaches, both from a theoretical and practical perspective, the most important principles and conceptual frameworks that can be considered in the application of social media metrics for scientific evaluation. We propose conceptually valid uses for social media metrics in research evaluation. The chapter discusses frameworks and uses of these metrics as well as principles and recommendations for the consideration and application of current (and potentially new) metrics in research evaluation.

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Section: Network-based Indicatorsmentioning

confidence: 99%

Section: Hashtag Coupling Analysismentioning

confidence: 99%

Section: Hashtag Coupling Analysismentioning

confidence: 99%

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Social Media Metrics for New Research Evaluation

Wouters

Zahedi

Costas

2019

Springer Handbook of Science and Technology Indicators

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“…For the naming of the areas, a heuristic was used that produces suggestions based on text mining services OpenCalais and Zemanta. All details of the technical implementation can be found in Kraker et al (2015).…”

Section: Educational Technology As Seen Through the Eyes Of The Readersmentioning

confidence: 99%

Educational technology as seen through the eyes of the readers

Kraker

2015

IJTEL

Self Cite

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In this paper, I present the evaluation of a novel knowledge domain visualization of educational technology. The interactive visualization is based on readership patterns in the online reference management system Mendeley. It comprises of 13 topic areas, spanning psychological, pedagogical, and methodological foundations, learning methods and technologies, and social and technological developments.The visualization was evaluated with (1) a qualitative comparison to knowledge domain visualizations based on citations, and (2) expert interviews. The results show that the co-readership visualization is a recent representation of pedagogical and psychological research in educational technology. Furthermore, the co-readership analysis covers more areas than comparable visualizations based on co-citation patterns. Areas related to computer science, however, are missing from the co-readership visualization and more research is needed to explore the interpretations of size and placement of research areas on the map. Biographical notes:Peter Kraker is a postdoc at Know-Center, Graz University of Technology. His main research interests are scholarly communication on the web, Science 2.0, and alternative metrics for science and research (altmetrics). He received his doctoral degree (Dr. rer. soc. oec.) from University of Graz in 2014. Peter has worked in a number of EU-funded projects studying online scholarly communication in educational technology, including the Network of Excellence STELLAR and the Marie Curie project TEAM. For his work, he was awarded a Panton Fellowship of the Open Knowledge Foundation and a Marshallplan Scholarship of the Austrian Marshallplan Foundation.

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“…Similar to bibliometric data, altmetric data can not only be used for research evaluation purposes, but also for networking or science mapping. Kraker, Schlögl, Jack, and Lindstaedt (2014) presented a methodology and prototype for creating knowledge domain visualizations based on readership statistics (from Mendeley). Haunschild and Bornmann (2015) generated a readership network which is based on Mendeley readers per (sub-)discipline for a large dataset of biomedical papers.…”

Section: Introductionmentioning

confidence: 99%

Networks of reader and country status: an analysis of Mendeley reader statistics

Haunschild

Bornmann

Leydesdorff

2015

PeerJ Computer Science

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The number of papers published in journals indexed by the Web of Science core collection is steadily increasing. In recent years, nearly two million new papers were published each year; somewhat more than one million papers when primary research papers are considered only (articles and reviews are the document types where primary research is usually reported or reviewed). However, who reads these papers? More precisely, which groups of researchers from which (self-assigned) scientific disciplines and countries are reading these papers? Is it possible to visualize readership patterns for certain countries, scientific disciplines, or academic status groups? One popular method to answer these questions is a network analysis. In this study, we analyze Mendeley readership data of a set of 1,133,224 articles and 64,960 reviews with publication year 2012 to generate three different networks: (1) The network based on disciplinary affiliations of Mendeley readers contains four groups: (i) biology, (ii) social sciences and humanities (including relevant computer sciences), (iii) bio-medical sciences, and (iv) natural sciences and engineering. In all four groups, the category with the addition "miscellaneous" prevails. (2) The network of co-readers in terms of professional status shows that a common interest in papers is mainly shared among PhD students, Master's students, and postdocs. (3) The country network focusses on global readership patterns: a group of 53 nations is identified as core to the scientific enterprise, including Russia and China as well as two thirds of the OECD (Organisation for Economic Cooperation and Development) countries.

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Visualization of co-readership patterns from an online reference management system

Cited by 20 publications

References 41 publications

Social Media Metrics for New Research Evaluation

Social Media Metrics for New Research Evaluation

Educational technology as seen through the eyes of the readers

Networks of reader and country status: an analysis of Mendeley reader statistics

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