Do Mendeley reader counts reflect the scholarly impact of conference papers? An investigation of computer science and engineering

This is an accepted manuscript of an article published by Springer in Scientometrics on 13/04/2017, available online: https://doi.org/10.1007/s11192-017-2367-1 The accepted version of the publication may differ from the final published version.Counts of Mendeley readers may give useful evidence about the impact of published re-search. Although previous studies have found significant positive correlations between counts of Mendeley readers and citation counts for journal articles, it is not known if this is equally true for conference papers. To fill this gap, Mendeley readership data and Scopus citation counts were extracted for both journal articles and conference papers published in 2011 in four fields for which conferences are important: Computer Science Applications; Computer Software; Building & Construction Engineering; and Industrial & Manufacturing Engineer-ing. Mendeley readership counts correlated moderately with citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. The correlations were much lower between Mendeley readers and citation counts for confer-ence papers than for journal articles in Building & Construction Engineering and Industrial & Manufacturing Engineering. Hence, there seem to be disciplinary differences in the useful-ness of Mendeley readership counts as impact indicators for conference papers, even between fields for which conferences are important

Are Mendeley reader counts high enough for research evaluations when articles are published?

This is an accepted manuscript of an article published by Emerald Publishing Limited in Aslib Journal of Information Management on 27/10/2017, available online: https://doi.org/10.1108/AJIM-01-2017-0028 The accepted version of the publication may differ from the final published version.Purpose –Mendeley reader counts have been proposed as early indicators for the impact of academic publications. In response, this article assesses whether there are enough Mendeley readers for research evaluation purposes during the month when an article is first published. Design/methodology/approach – Average Mendeley reader counts were compared to average Scopus citation counts for 104520 articles from ten disciplines during the second half of 2016. Findings - Articles attracted, on average, between 0.1 and 0.8 Mendeley readers per article in the month in which they first appeared in Scopus. This is about ten times more than the average Scopus citation count. Research limitations/implications – Other subjects may use Mendeley more or less than the ten investigated here. The results are dependent on Scopus’s indexing practices, and Mendeley reader counts can be manipulated and have national and seniority biases. Practical implications – Mendeley reader counts during the month of publication are more powerful than Scopus citations for comparing the average impacts of groups of documents but are not high enough to differentiate between the impacts of typical individual articles. Originality/value - This is the first multi-disciplinary and systematic analysis of Mendeley reader counts from the publication month of an article

Models as Social Actors in the Diffusion of AI Innovations: A Multilayer, Heterogeneous, Dynamic Network Perspective

Artificial Intelligence (AI) has emerged as a crucial facet of contemporary technological innovation, influencing diverse domains. Consequently, understanding the diffusion and evolution of AI innovations is vital. Scholarly publications have commonly served as proxies for studying these AI innovations. However, previous studies on publication diffusion have largely overlooked the role of models, which is particularly integral for AI innovations as they bridge upstream datasets and downstream applications. Moreover, models form an interdependent network due to their combinational evolution. This paper addresses this gap, examining how the location, movement, and speed of model movement in that model network affect the dissemination of AI research. Using a four-layer network—author collaborations, paper citations, model dependencies, and keyword co-occurrences—we examine 345,383 AI papers from 2000 to 2022. This research aims to contribute to the diffusion of innovation literature and dynamic network analysis, offering several novel insights and advancements

Mendeley reader counts for US computer science conference papers and journal articles

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://direct.mit.edu/qss/article/1/1/347/15566/Mendeley-reader-counts-for-US-computer-scienceAlthough bibliometrics are normally applied to journal articles when used to support research evaluations, conference papers are at least as important in fast-moving computingrelated fields. It is therefore important to assess the relative advantages of citations and altmetrics for computing conference papers to make an informed decision about which, if any, to use. This paper compares Scopus citations with Mendeley reader counts for conference papers and journal articles that were published between 1996 and 2018 in 11 computing fields and had at least one US author. The data showed high correlations between Scopus citation counts and Mendeley reader counts in all fields and most years, but with few Mendeley readers for older conference papers and few Scopus citations for new conference papers and journal articles. The results therefore suggest that Mendeley reader counts have a substantial advantage over citation counts for recently-published conference papers due to their greater speed, but are unsuitable for older conference papers

Mapping the impact of papers on various status groups in excellencemapping.net: a new release of the excellence mapping tool based on citation and reader scores

In over five years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014b) and Bornmann, Stefaner, de Moya Anegón, and Mutz (2014c, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profile pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained

Can web indicators be used to estimate the citation impact of conference papers in engineering?

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Although citation counts are widely used to support research evaluation, they can only reflect academic impacts, whereas research can also be useful outside academia. There is therefore a need for alternative indicators and empirical studies to evaluate them. Whilst many previous studies have investigated alternative indicators for journal articles and books, this thesis explores the importance and suitability of four web indicators for conference papers. These are readership counts from the online reference manager Mendeley and citation counts from Google Patents, Wikipedia and Google Books. To help evaluate these indicators for conference papers, correlations with Scopus citations were evaluated for each alternative indicator and compared with corresponding correlations between alternative indicators and citation counts for journal articles. Four subject areas that value conferences were chosen for the analysis: Computer Science Applications; Computer Software Engineering; Building & Construction Engineering; and Industrial & Manufacturing Engineering. There were moderate correlations between Mendeley readership counts and Scopus citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. For conference papers in Building & Construction Engineering and Industrial & Manufacturing Engineering, the correlations between Mendeley readers and citation counts are much lower than for journal articles. Thus, in fields where conferences are important, Mendeley readership counts are reasonable impact indicators for conference papers although they are better impact indicators for journal articles. Google Patent citations had low positive correlations with citation counts for both conference papers and journal articles in Software Engineering and Computer Science Applications. There were negative correlations for both conference papers and journal articles in Industrial and Manufacturing Engineering. However, conference papers in Building and Construction Engineering attracted no Google Patent citations. This suggests that there are disciplinary differences but little overall value for Google Patent citations as impact indicators in engineering fields valuing conferences. Wikipedia citations had correlations with Scopus citations that were statistically significantly positive only in Computer Science Applications, whereas the correlations were not statistically significantly different from zero in Building & Construction Engineering, Industrial & Manufacturing Engineering and Software Engineering. Conference papers were less likely to be cited in Wikipedia than journal articles were in all fields, although the difference was minor in Software Engineering. Thus, Wikipedia citations seem to have little value in engineering fields valuing conferences. Google Books citations had positive significant correlations with Scopus-indexed citations for conference papers in all fields except Building & Construction Engineering, where the correlations were not statistically significantly different from zero. Google Books citations seemed to be most valuable impact indicators in Computer Science Applications and Software Engineering, where the correlations were moderate, than in Industrial & Manufacturing Engineering, where the correlations were low. This means that Google Book citations are valuable indicators for conference papers in engineering fields valuing conferences. Although evidence from correlation tests alone is insufficient to judge the value of alternative indicators, the results suggest that Mendeley readers and Google Books citations may be useful for both journal articles and conference papers in engineering fields that value conferences, but not Wikipedia citations or Google Patent citations.Tetfund, Nigeri