The pros and cons of the use of altmetrics in research assessment

© 2020 The Authors. Published by Levi Library 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: http://doi.org/10.29024/sar.10Many indicators derived from the web have been proposed to supplement citation-based indicators in support of research assessments. These indicators, often called altmetrics, are available commercially from Altmetric.com and Elsevier’s Plum Analytics or can be collected directly. These organisations can also deliver altmetrics to support institutional selfevaluations. The potential advantages of altmetrics for research evaluation are that they may reflect important non-academic impacts and may appear before citations when an article is published, thus providing earlier impact evidence. Their disadvantages often include susceptibility to gaming, data sparsity, and difficulties translating the evidence into specific types of impact. Despite these limitations, altmetrics have been widely adopted by publishers, apparently to give authors, editors and readers insights into the level of interest in recently published articles. This article summarises evidence for and against extending the adoption of altmetrics to research evaluations. It argues that whilst systematicallygathered altmetrics are inappropriate for important formal research evaluations, they can play a role in some other contexts. They can be informative when evaluating research units that rarely produce journal articles, when seeking to identify evidence of novel types of impact during institutional or other self-evaluations, and when selected by individuals or groups to support narrative-based non-academic claims. In addition, Mendeley reader counts are uniquely valuable as early (mainly) scholarly impact indicators to replace citations when gaming is not possible and early impact evidence is needed. Organisations using alternative indicators need recruit or develop in-house expertise to ensure that they are not misused, however

The Values and Limits of Altmetrics

Altmetrics are tools for measuring the impact of research beyond scientific communities. In general, they measure online mentions of scholarly outputs, such as on online social networks, blogs, and news sites. Some stakeholders in higher education have championed altmetrics as a new way to understand research impact and as an alternative or supplement to bibliometrics. Contrastingly, others have criticized altmetrics for being ill conceived and limited in their use. This chapter explores the values and limits of altmetrics, including their role in evaluating, promoting, and disseminating research

Correlating altmetrics and h5-Index using Google Scholar metrics for journals in Library and Information Science

The purpose of this paper is to correlate altmetrics and h5-index using Google Scholar metrics for journals in Library and Information Science, in order to clarify the relative significance of altmetrics in evaluating research impact. This paper adopted the behavioural bibliometrics to analyse data that was collected from Google Scholar metrics for three systematically selected journals in LIS. We obtained altmetrics scores for selected articles from Altmetrics.com. This paper focuses on: (i) the extent in which altmetrics indicators correlate with the journal’s h5-index; (ii) the disproportions amongst altmetrics indicators, and; (iii) the comparison of article altmetrics scores in journals with different h5-index. The results of this paper reveal noteworthy independence of altmetrics from h5-index. Therefore, the journal’s h5-index does not impact or reflect on its article altmetrics. Amongst other altmetrics indicators, Mendeley dominates in all articles altmetrics. The results further confirmed the possibility of articles in journals with low h5-index to attained greater social media attention than articles in journals with high h5-index. This paper adds to the body of knowledge in LIS, informetrics in particular. It is hoped that the results of this study will help create better understanding of altmetrics and prevent its misuse

Career development tips for today's nursing academic: bibliometrics, altmetrics and social media

© 2016 John Wiley & Sons Ltd Aims: A discussion of bibliometrics, altmetrics and social media for the contemporary nursing scholar and academic researcher. Background: Today's nursing academic faces myriad challenges in balancing their daily life and, in recent years, academic survival has been increasingly challenged by the various research assessment exercises that evaluate the performance of knowledge institutions. As such, it is essential that today's nursing academic keep up to date with the core competencies needed for survival in a modern research career, particularly the intersecting triad of bibliometrics, altmetrics and social media. Design: Discussion paper. Data sources: Published literature and relevant websites. Implications for nursing: The rise of social media and altmetrics has important implications for contemporary nursing scholars who publish their research. Some fundamental questions when choosing a journal might be ‘does it have a Twitter and/or Facebook site, or a blog (or all three)’; and ‘does it have any other presence on social media, such as LinkedIn, Wikipedia, YouTube, ResearchGate and so on?’ Another consequence of embracing social media is that individual academics should also develop their own strategies for promoting and disseminating their work as widely as possible. Conclusion: The rising importance of social media and altmetrics can no longer be ignored, and today's nursing academic now has another facet to consider in their scholarly activities. Despite the changing nature of research dissemination, however, it is still important to recognize the undoubted value of established knowledge dissemination routes (that being the peer-reviewed publication)

Studying Relationship between Citation and Altmetrics of Top Chemistry Researches’ Articles

Abstract: The main objective of the present research is to examine the relationship between the number of citations and the level of altmetrics for testing the validity of these new metrics, at least in terms of being alignment with the test established index. The present research population consist of articles from the top chemistry writers that were profiled at the Scopus Citation Database in 2010. Sample research is the articles by 20 top author. The present research is applied in terms of purpose, and is descriptive and correlative in terms of data collection. Data extraction was performed using Webometric analyst software and citation data was collected from Scopus. SPSS software was used to analyze the data. The research findings show that the articles in question have little presence on social networks. In terms of the amount of attendance and distribution Mendeley, CiteUlike, Twitter, Facebook, Blogs, Google Plus and News, had the largest number of articles and altmetrics respectively. Also, the results show that Mendeley and Twitter have the most relationship with citations. Also, articles have at least one higher citation average altmetric (25.14%) than those with no altmetric (7.58%). In terms of citations\u27 relationship, the Spearman correlation test showed a strong correlation between the number of Mendeley readers, news, and citations. Also, there was a weak correlation between Twitter, CiteUlike, and citations. Finally, there was not a meaningful relationship between Facebook posts, blog posts, Google plus, and citations

Exploring Features for Predicting Policy Citations

In this study we performed an initial investigation and evaluation of altmetrics and their relationship with public policy citation of research papers. We examined methods for using altmetrics and other data to predict whether a research paper is cited in public policy and applied receiver operating characteristic curve on various feature groups in order to evaluate their potential usefulness. From the methods we tested, classifying based on tweet count provided the best results, achieving an area under the ROC curve of 0.91.Comment: 2 pages, accepted to JCDL '1