Evidence map›Paper›PMID 33534795›Full record

ArticlePloS one2021

Neologisms are epidemic: Modeling the life cycle of neologisms in China 2008-2016.

Menghan Jiang, Xiang Ying Shen, Kathleen Ahrens, Chu-Ren Huang

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Menghan JiangDepartment of Chinese Language and Literature, Peking University, Beijing, China.ORCID 0000-0001-5005-147X
Xiang Ying ShenDepartment of Physics, Chinese University of Hong Kong, Hong Kong, China.
Kathleen AhrensDepartment of English, The Hong Kong Polytechnic University, Hong Kong, China.
Chu-Ren HuangDepartment of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0002-8526-5520

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

InternetLanguageModels, TheoreticalChinaEpidemicsTime Factors

Identifiers

PMID33534795
PMCPMC7857598

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.