Skewness distribution of four key altmetric indicators: an in-progress analysis across 22 fields in a national context

First of all, it should be mentioned that although this study uses a large sample of scientific publications (a total of 237,232), they are all Spanish publications. Therefore, the results may not be extrapolated. Recent studies show that altmetric indicators may have different patterns depending on the country. Countries such as Spain, France and Germany may have different altmetric patterns to Anglo-Saxon countries. However, various findings have been demonstrated that can be extrapolated to all contexts regardless of their specific features. Each altmetric indicator has its own pattern of asymmetry and is not the same in all scientific areas. The values are very different depending on the area and the indicator. Another important aspect pointed out by the study is that, compared to citations, the distributions of altmetric indicators are always less skewed and less pronounced. It also seems that citations are more similar to Twitter mentions. This paper is useful to provide a general mapping by indicator and area of the phenomenon of asymmetry in the world of altmetrics. It may be of use when establishing the field validity of certain indicators or when using statistical indicators such as averages. It will also help to decide whether it is necessary to introduce standardisation procedures for indicators such as those used by Costas and Bornmam and Leydesdorff. This work will be continued in the future using the complete Altmetric.com database and introducing a larger number of altmetric indicators

An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics

Sufficient data presence is one of the key preconditions for applying metrics in practice. Based on both Altmetric.com data and Mendeley data collected up to 2019, this paper presents a state-of-the-art analysis of the presence of 12 kinds of altmetric events for nearly 12.3 million Web of Science publications published between 2012 and 2018. Results show that even though an upward trend of data presence can be observed over time, except for Mendeley readers and Twitter mentions, the overall presence of most altmetric data is still low. The majority of altmetric events go to publications in the fields of Biomedical and Health Sciences, Social Sciences and Humanities, and Life and Earth Sciences. As to research topics, the level of attention received by research topics varies across altmetric data, and specific altmetric data show different preferences for research topics, on the basis of which a framework for identifying hot research topics is proposed and applied to detect research topics with higher levels of attention garnered on certain altmetric data source. Twitter mentions and policy document citations were selected as two examples to identify hot research topics of interest of Twitter users and policy-makers, respectively, shedding light on the potential of altmetric data in monitoring research trends of specific social attention

Testing for universality of Mendeley readership distributions

Altmetrics promise useful support for assessing the impact of scientific works, including beyond the scholarly community and with very limited citation windows. Unfortunately, altmetrics scores are currently available only for recent articles and cannot be used as covariates in predicting long term impact of publications. However, the study of their statistical properties is a subject of evident interest to scientometricians. Applying the same approaches used in the literature to assess the universality of citation distributions, the intention here is to test whether the universal distribution also holds for Mendeley readerships. Results of the analysis carried out on a sample of publications randomly extracted from the Web of Science confirm that readerships seem to share similar shapes across fields and can be rescaled to a common and universal form. Such rescaling results as not particularly effective on the right tails. In other regions, rescaling causes a good collapse of field specific distributions, even for very recent publications