Proposal of composed altmetric indicators based on prevalence and impact dimensions

Abstract

The aim of this study is to introduce two groups of impact indicators, Weighted Altmetric Impact (WAI) and Inverse Altmetric Impact (IAI). WAI is based in weights from the contributions of each metric to different components or impact dimensions (Principal Component Analysis). IAI is calculated according to the inverse prevalence of each metric in different impact dimensions (TF/IDF). These indicators were tested against 29,500 articles, using metrics from Altmetric.com, PlumX and CED. Altmetric Attention Score (AAScore) was also obtained to compare the resulting scores. Several statistical analyses were applied to value the advantages and limitations of these indicators. Frequency distributions showed that each group of metrics (Scientific Impact, Media Impact and Usage Impact) follows power law trends although with particular patterns. Correlation matrices have depicted associations between metrics and indicators. Multidimensional scaling (MDS) has plotted these interactions to visualize distances between indicators and metrics in each dimension. The 2018 Altmetric Top 100 was used to distinguish differences between rankings from AAScore and the proposed indicators. The paper concludes that the theoretical assumptions of dimensions and prevalence are suitable criteria to design transparent and reproducible impact indicators.