Extracting information from S-curves of language change

Ghanbarnejad, Fakhteh; Gerlach, Martin; Miotto, José M.; Altmann, Eduardo G.

doi:10.1098/rsif.2014.1044

Cited by 39 publications

(38 citation statements)

References 30 publications

(71 reference statements)

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“…Ghanbarnejad et al [26] analyze the dynamics of language change, to understand the variety of curves that we see when we plot language change over time (as in our Fig 1). They introduce various mathematical models that can be used to gain a deeper understanding of these curves.…”

Section: Related Work On the Evolution Of Wordsmentioning

confidence: 99%

The natural selection of words: Finding the features of fitness

Turney

Mohammad

2019

PLoS ONE

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We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future leader of a synset: the word in the synset that will have the highest frequency. The algorithm uses features based on a word’s length, the characters in the word, and the historical frequencies of the word. It can predict change of leadership (including the identity of the new leader) fifty years in the future, with an F-score considerably above random guessing. Analysis of the learned models provides insight into the causes of change in the leader of a synset. The algorithm confirms observations linguists have made, such as the trend to replace the -ise suffix with -ize, the rivalry between the -ity and -ness suffixes, and the struggle between economy (shorter words are easier to remember and to write) and clarity (longer words are more distinctive and less likely to be confused with one another). The results indicate that integration of the Google Books Ngram Corpus with WordNet has significant potential for improving our understanding of how language evolves.

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Section: Related Work On the Evolution Of Wordsmentioning

confidence: 99%

The natural selection of words: Finding the features of fitness

Turney

Mohammad

2019

PLoS ONE

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“…We show that these mechanisms leave robust and markedly distinct stylized signatures in the data, and we propose a simple evolutionary model able to reproduce quantitatively all of the empirical observations. When a formal institution drives the norm change, the old convention is rapidly abandoned in favor of the new one [7,[27][28][29][30]. This determines a universal process of norm adoption which is independent of both word frequency and corpus size.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of norm change in the cultural evolution of language

Amato

Lacasa

Dı́az-Guilera

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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What happens when a new social convention replaces an old one? While the possible forces favoring norm change-such as institutions or committed activists-have been identified for a long time, little is known about how a population adopts a new convention, due to the difficulties of finding representative data. Here, we address this issue by looking at changes that occurred to 2,541 orthographic and lexical norms in English and Spanish through the analysis of a large corpora of books published between the years 1800 and 2008. We detect three markedly distinct patterns in the data, depending on whether the behavioral change results from the action of a formal institution, an informal authority, or a spontaneous process of unregulated evolution. We propose a simple evolutionary model able to capture all of the observed behaviors, and we show that it reproduces quantitatively the empirical data. This work identifies general mechanisms of norm change, and we anticipate that it will be of interest to researchers investigating the cultural evolution of language and, more broadly, human collective behavior.

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“…The Bass model has been extensively employed in marketing and social sciences [6,7], resulting in large empirical databases of its coefficients, measured in different situations.…”

Section: Introductionmentioning

confidence: 99%

Innovation diffusion equations on correlated scale-free networks

Bertotti¹,

Brunner²,

Modanese³

2016

Physics Letters A

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We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.Comment: 13 pages, 5 figure

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Extracting information from S-curves of language change

Cited by 39 publications

References 30 publications

The natural selection of words: Finding the features of fitness

The natural selection of words: Finding the features of fitness

The dynamics of norm change in the cultural evolution of language

Innovation diffusion equations on correlated scale-free networks

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