  Power laws are a characteristic distribution that are ubiquitous, in that
they are found almost everywhere, in both natural as well as in man-made
systems. They tend to emerge in large, connected and self-organizing systems,
for example, scholarly publications. Citations to scientific papers have been
found to 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. The
distributional character of altmetrics has not been studied yet as altmetrics
are among the newest indicators related to scholarly publications. Here we
select a data sample from the altmetrics aggregator Altmetrics.com containing
records from the platforms Facebook, Twitter, News, Blogs, etc., and the
composite variable Alt-score for the period 2016. 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. The log-log plot of the data, 'mentions' vs. number of
papers, falls on an approximately linear line, suggesting the plausibility of a
power law distribution. The fit is not very good in all cases due to large
fluctuations in the tail. We show that fit to the power law can be improved by
truncating the data series to eliminate large fluctuations in the tail. We
conclude that altmetric distributions also follow power laws with a fairly good
fit over a wide range of values. More rigorous methods of determination may not
be necessary at present.
