Does Microsoft Academic find early citations?

This is an accepted manuscript of an article published by Springer in Scientometrics on 27/10/2017, available online: https://doi.org/10.1007/s11192-017-2558-9 The accepted version of the publication may differ from the final published version.This article investigates whether Microsoft Academic can use its web search component to identify early citations to recently published articles to help solve the problem of delays in research evaluations caused by the need to wait for citation counts to accrue. The results for 44,398 articles in Nature, Science and seven library and information science journals 1996-2017 show that Microsoft Academic and Scopus citation counts are similar for all years, with no early citation advantage for either. In contrast, Mendeley reader counts are substantially higher for more recent articles. Thus, Microsoft Academic appears to be broadly like Scopus for citation count data, and is apparently not more able to take advantage of online preprints to find early citations

ResearchGate versus Google Scholar: Which finds more early citations?

ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless, preprint sharing in ResearchGate is substantial enough for authors to take seriously

Are Mendeley Reader Counts Useful Impact Indicators in all Fields?

Reader counts from the social reference sharing site Mendeley are known to be valuable for early research evaluation. They have strong correlations with citation counts for journal articles but appear about a year before them. There are disciplinary differences in the value of Mendeley reader counts but systematic evidence is needed at the level of narrow fields to reveal its extent. In response, this article compares Mendeley reader counts with Scopus citation counts for journal articles from 2012 in 325 narrow Scopus fields. Despite strong positive correlations in most fields, averaging 0.671, the correlations in some fields are as weak as 0.255. Technical reasons explain most weaker correlations, suggesting that the underlying relationship is almost always strong. The exceptions are caused by unusually high educational or professional use or topics of interest within countries that avoid Mendeley. The findings suggest that if care is taken then Mendeley reader counts can be used for early citation impact evidence in almost all fields and for related impact in some of the remainder. As an additional application of the results, cross-checking with Mendeley data can be used to identify indexing anomalies in citation databases

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

© 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://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance

Can Microsoft Academic be used for citation analysis of preprint archives? The case of the Social Science Research Network

This is an accepted manuscript of an article published by Springer in Scientometrics on 07/03/2018, available online: https://doi.org/10.1007/s11192-018-2704-z The accepted version of the publication may differ from the final published version.Preprint archives play an important scholarly communication role within some fields. The impact of archives and individual preprints are difficult to analyse because online repositories are not indexed by the Web of Science or Scopus. In response, this article assesses whether the new Microsoft Academic can be used for citation analysis of preprint archives, focusing on the Social Science Research Network (SSRN). Although Microsoft Academic seems to index SSRN comprehensively, it groups a small fraction of SSRN papers into an easily retrievable set that has variations in character over time, making any field normalisation or citation comparisons untrustworthy. A brief parallel analysis of arXiv suggests that similar results would occur for other online repositories. Systematic analyses of preprint archives are nevertheless possible with Microsoft Academic when complete lists of archive publications are available from other sources because of its promising coverage and citation results

Early Mendeley readers correlate with later citation counts

This is an accepted manuscript of an article published by Springer in Scientometrics on 26/03/2018, available online: https://doi.org/10.1007/s11192-018-2715-9 The accepted version of the publication may differ from the final published version.Counts of the number of readers registered in the social reference manager Mendeley have been proposed as an early impact indicator for journal articles. Although previous research has shown that Mendeley reader counts for articles tend to have a strong positive correlation with synchronous citation counts after a few years, no previous studies have compared early Mendeley reader counts with later citation counts. In response, this first diachronic analysis compares reader counts within a month of publication with citation counts after 20 months for ten fields. There were moderate or strong correlations in eight out of ten fields, with the two exceptions being the smallest categories (n=18, 36) with wide confidence intervals. The correlations are higher than the correlations between later citations and early citations, showing that Mendeley reader counts are more useful early impact indicators than citation counts

Emotional persistence in online chatting communities

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional "tone" of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.Comment: 34 pages, 4 main and 12 supplementary figure

Proposal for a multilevel university cybermetric analysis model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-012-0868-5Universities’ online seats have gradually become complex systems of dynamic information where all their institutions and services are linked and potentially accessible. These online seats now constitute a central node around which universities construct and document their main activities and services. This information can be quantitative measured by cybermetric techniques in order to design university web rankings, taking the university as a global reference unit. However, previous research into web subunits shows that it is possible to carry out systemic web analyses, which open up the possibility of carrying out studies which address university diversity, necessary for both describing the university in greater detail and for establishing comparable ranking units. 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Correlations between structure and dynamics in complex networks

Previous efforts in complex networks research focused mainly on the topological features of such networks, but now also encompass the dynamics. In this Letter we discuss the relationship between structure and dynamics, with an emphasis on identifying whether a topological hub, i.e. a node with high degree or strength, is also a dynamical hub, i.e. a node with high activity. We employ random walk dynamics and establish the necessary conditions for a network to be topologically and dynamically fully correlated, with topological hubs that are also highly active. Zipf's law is then shown to be a reflection of the match between structure and dynamics in a fully correlated network, as well as a consequence of the rich-get-richer evolution inherent in scale-free networks. We also examine a number of real networks for correlations between topology and dynamics and find that many of them are not fully correlated.Comment: 16 pages, 7 figures, 1 tabl

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries