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Neologisms Are Epidemic: Modeling the Life Cycle of Neologisms in China 2008-2016

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Journal PLoS One
Date 2021 Feb 3
PMID 33534795
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Abstract

This paper adopts models from epidemiology to account for the development and decline of neologisms based on internet usage. The research design focuses on the issue of whether a host-driven epidemic model is well-suited to explain human behavior regarding neologisms. We extracted the search frequency data from Google Trends that covers the ninety most influential Chinese neologisms from 2008-2016 and found that the majority of them possess a similar rapidly rising-decaying pattern. The epidemic model is utilized to fit the evolution of these internet-based neologisms. The epidemic model not only has good fitting performance to model the pattern of rapid growth, but also is able to predict the peak point in the neologism's life cycle. This result underlines the role of human agents in the life cycle of neologisms and supports the macro-theory that the evolution of human languages mirrors the biological evolution of human beings.

References
1.
Lieberman E, Michel J, Jackson J, Tang T, Nowak M . Quantifying the evolutionary dynamics of language. Nature. 2007; 449(7163):713-6. PMC: 2460562. DOI: 10.1038/nature06137. View

2.
Cavalli-Sforza L, Piazza A, Menozzi P, Mountain J . Reconstruction of human evolution: bringing together genetic, archaeological, and linguistic data. Proc Natl Acad Sci U S A. 1988; 85(16):6002-6. PMC: 281893. DOI: 10.1073/pnas.85.16.6002. View

3.
Azumagakito T, Suzuki R, Arita T . An integrated model of gene-culture coevolution of language mediated by phenotypic plasticity. Sci Rep. 2018; 8(1):8025. PMC: 5966417. DOI: 10.1038/s41598-018-26233-7. View

4.
Pagel M . Human language as a culturally transmitted replicator. Nat Rev Genet. 2009; 10(6):405-15. DOI: 10.1038/nrg2560. View

5.
Brady W, Wills J, Jost J, Tucker J, Van Bavel J . Emotion shapes the diffusion of moralized content in social networks. Proc Natl Acad Sci U S A. 2017; 114(28):7313-7318. PMC: 5514704. DOI: 10.1073/pnas.1618923114. View