Archives des mots-clés : Altmetrics

The Most Widely Disseminated COVID-19-Related Scientific Publications in Online Media: A Bibliometric Analysis of the Top 100 Articles with the Highest Altmetric Attention Scores

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Authors : Ji Yoon Moon, Dae Young Yoon, Ji Hyun Hong, Kyoung Ja Lim, Sora Baek, Young Lan Seo, Eun Joo Yun

The novel coronavirus disease 2019 (COVID-19) is a global pandemic. This study’s aim was to identify and characterize the top 100 COVID-19-related scientific publications, which had received the highest Altmetric Attention Scores (AASs).

Hence, we searched Altmetric Explorer using search terms such as “COVID” or “COVID-19” or “Coronavirus” or “SARS-CoV-2” or “nCoV” and then selected the top 100 articles with the highest AASs. For each article identified, we extracted the following information: the overall AAS, publishing journal, journal impact factor (IF), date of publication, language, country of origin, document type, main topic, and accessibility.

The top 100 articles most frequently were published in journals with high (>10.0) IF (n = 67), were published between March and July 2020 (n = 67), were written in English (n = 100), originated in the United States (n = 45), were original articles (n = 59), dealt with treatment and clinical manifestations (n = 33), and had open access (n = 98).

Our study provides important information pertaining to the dissemination of scientific knowledge about COVID-19 in online media.

URL : The Most Widely Disseminated COVID-19-Related Scientific Publications in Online Media: A Bibliometric Analysis of the Top 100 Articles with the Highest Altmetric Attention Scores

DOI : https://doi.org/10.3390/healthcare9020239

Conjoint analysis of researchers’ hidden preferences for bibliometrics, altmetrics, and usage metrics

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Authors : Steffen Lemke, Athanasios Mazarakis, Isabella Peters

The amount of annually published scholarly articles is growing steadily, as is the number of indicators through which impact of publications is measured. Little is known about how the increasing variety of available metrics affects researchers’ processes of selecting literature to read.

We conducted ranking experiments embedded into an online survey with 247 participating researchers, most from social sciences. Participants completed series of tasks in which they were asked to rank fictitious publications regarding their expected relevance, based on their scores regarding six prototypical metrics.

Through applying logistic regression, cluster analysis, and manual coding of survey answers, we obtained detailed data on how prominent metrics for research impact influence our participants in decisions about which scientific articles to read.

Survey answers revealed a combination of qualitative and quantitative characteristics that researchers consult when selecting literature, while regression analysis showed that among quantitative metrics, citation counts tend to be of highest concern, followed by Journal Impact Factors.

Our results suggest a comparatively favorable view of many researchers on bibliometrics and widespread skepticism toward altmetrics.

The findings underline the importance of equipping researchers with solid knowledge about specific metrics’ limitations, as they seem to play significant roles in researchers’ everyday relevance assessments.

URL : Conjoint analysis of researchers’ hidden preferences for bibliometrics, altmetrics, and usage metrics

DOI : https://doi.org/10.1002/asi.24445

Preprints as accelerator of scholarly communication: An empirical analysis in Mathematics

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Authors : Zhiqi Wang, Yue Chen, Wolfgang Glänzel

In this study we analyse the key driving factors of preprints in enhancing scholarly communication. To this end we use four groups of metrics, one referring to scholarly communication and based on bibliometric indicators (Web of Science and Scopus citations), while the others reflect usage (usage counts in Web of Science), capture (Mendeley readers) and social media attention (Tweets).

Hereby we measure two effects associated with preprint publishing: publication delay and impact. We define and use several indicators to assess the impact of journal articles with previous preprint versions in arXiv. In particular, the indicators measure several times characterizing the process of arXiv preprints publishing and the reviewing process of the journal versions, and the ageing patterns of citations to preprints.

In addition, we compare the observed patterns between preprints and non-OA articles without any previous preprint versions in arXiv. We could observe that the “early-view” and “open-access” effects of preprints contribute to a measurable citation and readership advantage of preprints.

Articles with preprint versions are more likely to be mentioned in social media and have shorter Altmetric attention delay. Usage and capture prove to have only moderate but stronger correlation with citations than Tweets. The different slopes of the regression lines between the different indicators reflect different order of magnitude of usage, capture and citation data.

URL : https://arxiv.org/abs/2011.11940

‘I Updated the ‘: The Evolution of References in the English Wikipedia and the Implications for Altmetrics

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Authors : Olga Zagovora, Roberto Ulloa, Katrin Weller, Fabian Flöck

With this work, we present a publicly available dataset of the history of all the references (more than 55 million) ever used in the English Wikipedia until June 2019. We have applied a new method for identifying and monitoring references in Wikipedia, so that for each reference we can provide data about associated actions: creation, modifications, deletions, and reinsertions.

The high accuracy of this method and the resulting dataset was confirmed via a comprehensive crowdworker labelling campaign. We use the dataset to study the temporal evolution of Wikipedia references as well as users’ editing behaviour.

We find evidence of a mostly productive and continuous effort to improve the quality of references: (1) there is a persistent increase of reference and document identifiers (DOI, PubMedID, PMC, ISBN, ISSN, ArXiv ID), and (2) most of the reference curation work is done by registered humans (not bots or anonymous editors).

We conclude that the evolution of Wikipedia references, including the dynamics of the community processes that tend to them should be leveraged in the design of relevance indexes for altmetrics, and our dataset can be pivotal for such effort.

URL : https://arxiv.org/abs/2010.03083

Open Access Books in the Humanities and Social Sciences: an Open Access Altmetric Advantage

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Author : Michael Taylor

The last decade has seen two significant phenomena emerge in research communication: the rise of open access (OA) publishing, and evidence of online sharing in the form of altmetrics. There has been limited examination of the effect of OA on online sharing for journal articles, and little for books.

This paper examines the altmetrics of a set of 32,222 books (of which 5% are OA) and a set of 220,527 chapters (of which 7% are OA) indexed by the scholarly database Dimensions in the Social Sciences and Humanities.

Both OA books and chapters have significantly higher use on social networks, higher coverage in the mass media and blogs, and evidence of higher rates of social impact in policy documents. OA chapters have higher rates of coverage on Wikipedia than their non-OA equivalents, and are more likely to be shared on Mendeley.

Even within the Humanities and Social Sciences, disciplinary differences in altmetric activity are evident. The effect is confirmed for chapters, although sampling issues prevent the strong conclusion that OA facilitates extra attention at whole book level, the apparent OA altmetrics advantage suggests that the move towards OA is increasing social sharing and broader impact.

URL  : https://arxiv.org/abs/2009.10442

The relationship between bioRxiv preprints, citations and altmetrics

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Authors : Nicholas Fraser, Fakhri Momeni, Philipp Mayr, Isabella Peters

A potential motivation for scientists to deposit their scientific work as preprints is to enhance its citation or social impact. In this study we assessed the citation and altmetric advantage of bioRxiv, a preprint server for the biological sciences.

We retrieved metadata of all bioRxiv preprints deposited between November 2013 and December 2017, and matched them to articles that were subsequently published in peer-reviewed journals.

Citation data from Scopus and altmetric data from Altmetric.com were used to compare citation and online sharing behavior of bioRxiv preprints, their related journal articles, and nondeposited articles published in the same journals. We found that bioRxiv-deposited journal articles had sizably higher citation and altmetric counts compared to nondeposited articles.

Regression analysis reveals that this advantage is not explained by multiple explanatory variables related to the articles’ publication venues and authorship. Further research will be required to establish whether such an effect is causal in nature.

bioRxiv preprints themselves are being directly cited in journal articles, regardless of whether the preprint has subsequently been published in a journal. bioRxiv preprints are also shared widely on Twitter and in blogs, but remain relatively scarce in mainstream media and Wikipedia articles, in comparison to peer-reviewed journal articles.

DOI : https://doi.org/10.1162/qss_a_00043

Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 literature

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Authors : Daniel Torres-Salinas, Nicolas Robinson-Garcia, Pedro A. Castillo-Valdivieso

We present an analysis on the uptake of open access on COVID-19 related literature as well as the social media attention they gather when compared with non OA papers.

We use a dataset of publications curated by Dimensions and analyze articles and preprints. Our sample includes 11,686 publications of which 67.5% are openly accessible.

OA publications tend to receive the largest share of social media attention as measured by the Altmetric Attention Score. 37.6% of OA publications are bronze, which means toll journals are providing free access.

MedRxiv contributes to 36.3% of documents in repositories but papers in BiorXiv exhibit on average higher AAS. We predict the growth of COVID-19 literature in the following 30 days estimating ARIMA models for the overall publications set, OA vs. non OA and by location of the document (repository vs. journal).

We estimate that COVID-19 publications will double in the next 20 days, but non OA publications will grow at a higher rate than OA publications. We conclude by discussing the implications of such findings on the dissemination and communication of research findings to mitigate the coronavirus outbreak.

DOI : https://doi.org/10.1101/2020.04.23.057307