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.
翻译:权力法是一种无处不在的典型分布法, 因为它在自然和人造系统中几乎到处都有, 自然和人造系统中都是如此。 它们往往出现在大型、 连接和自组织系统中, 例如学术出版物。 科学论文的引文被发现遵循权力法, 即具有一定引用度的论文数量与x的负功率成比例。 等量法的分布特性尚未研究过, 因为平方值是学术范围出版物的最新指标之一。 我们在这里从高量法中选择一个数据样本。 我们从高量法中选择一个数据样本, 包括来自平台脸书、 Twitter、 新闻、 博客等的纪录和2016年时期的综合可变数。 个人和各种平台上的“ 参考值” 组合数据序列符合权力法的分布, 以及使用最小方形回归法确定得的参数和美度。 数据、 “ 指数” 等值的日志图、 “ 数字” 和文件的校正值的校正范围。 我们也可以在一条直线上得出一个更准确的分布法系, 。 我们可以用一个更精确的校正的校正的校正的校正的校正的校正的判法, 。