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Extracting information from S-curves of language change

机译:从语言变化的S曲线中提取信息

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摘要

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period and slow end). In this paper, we analyse how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g. the Bass dynamics on complex networks), we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism.
机译:公认的是,创新采用S曲线来描述(慢启动,加速周期和慢结束)。在本文中,我们分析了从S曲线的定量描述中可以获得多少关于创新传播动力学的信息。我们专注于语言创新的采用,过去200年来详细的书面文字数据库可实现前所未有的统计精度。将数据分析与简单模型的模拟(例如复杂网络上的Bass动力学)相结合,我们可以确定采用S曲线中内生和外生因素的特征。我们提出了一种量化这些因素强度的方法,并提出了三种不同的方法来从S曲线进行估算。我们得到的案例中,外源因素占主导地位(在采用德国正字法改革和一个不规则动词的情况下)和内源性因素占主导地位的情况(在采用俄语名称罗马化的惯例中以及在大多数研究中的正则化中)动词)。这些结果表明,S曲线的形状不是通用的,并且包含有关采用机制的信息。

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