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

Abstract:  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. View Full-Text

 

Conjoint analysis of researchers’ hidden preferences for bibliometrics, altmetrics, and usage metrics – Lemke – – Journal of the Association for Information Science and Technology – Wiley Online Library

Abstract:  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.

 

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications – Fang – – Journal of the Association for Information Science and Technology – Wiley Online Library

Abstract:  To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

 

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications – Fang – – Journal of the Association for Information Science and Technology – Wiley Online Library

Abstract:  To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

 

Communicating Scientific Uncertainty in an Age of COVID-19: An Investigation into the Use of Preprints by Digital Media Outlets

Abstract:  In this article, we investigate the surge in use of COVID-19-related preprints by media outlets. Journalists are a main source of reliable public health information during crises and, until recently, journalists have been reluctant to cover preprints because of the associated scientific uncertainty. Yet, uploads of COVID-19 preprints and their uptake by online media have outstripped that of preprints about any other topic. Using an innovative approach combining altmetrics methods with content analysis, we identified a diversity of outlets covering COVID-19-related preprints during the early months of the pandemic, including specialist medical news outlets, traditional news media outlets, and aggregators. We found a ubiquity of hyperlinks as citations and a multiplicity of framing devices for highlighting the scientific uncertainty associated with COVID-19 preprints. These devices were rarely used consistently (e.g., mentioning that the study was a preprint, unreviewed, preliminary, and/or in need of verification). About half of the stories we analyzed contained framing devices emphasizing uncertainty. Outlets in our sample were much less likely to identify the research they mentioned as preprint research, compared to identifying it as simply “research.” This work has significant implications for public health communication within the changing media landscape. While current best practices in public health risk communication promote identifying and promoting trustworthy sources of information, the uptake of preprint research by online media presents new challenges. At the same time, it provides new opportunities for fostering greater awareness of the scientific uncertainty associated with health research findings.

 

Early Indicators of Scientific Impact: Predicting Citations with Altmetrics

Abstract:  Identifying important scholarly literature at an early stage is vital to the academic research community and other stakeholders such as technology companies and government bodies. Due to the sheer amount of research published and the growth of ever-changing interdisciplinary areas, researchers need an efficient way to identify important scholarly work. The number of citations a given research publication has accrued has been used for this purpose, but these take time to occur and longer to accumulate. In this article, we use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive. We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks. We also find that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Twitter, Wikipedia, and across different countries.

 

Full article: An Institutional Repository Publishing Model for Imperial College London Grey Literature

Abstract:  In 2019 we became increasingly aware of authors at Imperial College London choosing to publish grey literature through local website PDF or full text hosting. Recognising the need to improve the institutional open access repository as a venue of choice to publish or co-publish grey literature, we developed a publishing model of identifiers (DOIs and ORCIDs) and metrics (indexing, citations and Altmetric coverage). Some of the incentives already existed in the repository but had not previously been explicitly communicated as benefits; whilst others required technical infrastructure development and scholarly communications education for authors. As of September 2020, a 206% increase in deposit of one type of grey literature has been observed on the previous full year, including Imperial’s influential COVID-19 reports.

 

The transformative power of values-enacted scholarship | Humanities and Social Sciences Communications

Abstract:  The current mechanisms by which scholars and their work are evaluated across higher education are unsustainable and, we argue, increasingly corrosive. Relying on a limited set of proxy measures, current systems of evaluation fail to recognize and reward the many dependencies upon which a healthy scholarly ecosystem relies. Drawing on the work of the HuMetricsHSS Initiative, this essay argues that by aligning values with practices, recognizing the vital processes that enrich the work produced, and grounding our indicators of quality in the degree to which we in the academy live up to the values for which we advocate, a values-enacted approach to research production and evaluation has the capacity to reshape the culture of higher education.

 

[2011.11940] Preprints as accelerator of scholarly communication: An empirical analysis in Mathematics

Abstract:  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.

 

Towards societal impact through open research | Springer Nature | For Researchers | Springer Nature

“Open research is fundamentally changing the way that researchers communicate and collaborate to advance the pace and quality of discovery. New and dynamic open research-driven workflows are emerging, thus increasing the findability, accessibility, and reusability of results. Distribution channels are changing too, enabling others — from patients to businesses, to teachers and policy makers — to increasingly benefit from new and critical insights. This in turn has dramatically increased the societal impact of open research. But what remains less clear is the exact nature and scope of this wider impact as well as the societal relevance of the underpinning research….”