An empirical analysis of existence of power laws in social media mentions to scholarly articles

Abstract

Power laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit determined using least squares regression. We also explore fit to other distributions like the log-normal and Hooked Power Law. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles and we conclude that altmetric distributions also follow power law with a fairly good fit over a wide range of values. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved.