TY - JOUR
T1 - Emergent neologism
T2 - A study of an emerging meaning with competing forms based on the first six months of COVID-19
AU - Lei, Siyu
AU - Yang, Ruiying
AU - Huang, Chu Ren
N1 - Funding Information:
First and foremost, we are very grateful to Professor Kathleen Ahrens for her comments on earlier versions of this work. Our special appreciation also goes to Dr. Xiaowen Zhu for her advice on the regression analysis, to Dr. Yawei Yang for helping refine the figures, and to Dr. Menghan Jiang and Ms. Annie Xiaowen Wang for their comments on the diction. Moreover, we would like to express our sincere thanks to the anonymous reviewers and the editor for their feedback. All errors remain our own.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/7
Y1 - 2021/7
N2 - This paper investigates the emergence of COVID-19 neologisms. It focuses on the strategies used to coin emerging neologisms, the relationship between the strategies and the usage preferences, as well as the correlation between internet usage data and epidemiological data. The internet usage data were collected from December 2019 to June 2020 from the Baidu Index, covering the usage of all five categories of the COVID-19 name variants. The epidemiological data, from the Chinese Center for Disease Control and Prevention, are statistics of newly confirmed cases, newly suspected cases, new deaths, and currently suspected cases at a given time. The study identified three strategies in the coinage of neologisms: categorization, avoidance, and synthesis. In addition, a strong correlation between emergent neologisms and pandemic developments was discovered with a binomial model, and the emerging neologisms demonstrated a skewed S-curve life cycle, which is different from the established S-curve model of replacement changes. In sum, by leveraging internet usage data, this first study of the life cycle of emergent neologisms has several contributions: A theory of how new words emerge, the correlation between emergent neologisms and emerging events, and the potential of modeling language use for epidemiological predictions.
AB - This paper investigates the emergence of COVID-19 neologisms. It focuses on the strategies used to coin emerging neologisms, the relationship between the strategies and the usage preferences, as well as the correlation between internet usage data and epidemiological data. The internet usage data were collected from December 2019 to June 2020 from the Baidu Index, covering the usage of all five categories of the COVID-19 name variants. The epidemiological data, from the Chinese Center for Disease Control and Prevention, are statistics of newly confirmed cases, newly suspected cases, new deaths, and currently suspected cases at a given time. The study identified three strategies in the coinage of neologisms: categorization, avoidance, and synthesis. In addition, a strong correlation between emergent neologisms and pandemic developments was discovered with a binomial model, and the emerging neologisms demonstrated a skewed S-curve life cycle, which is different from the established S-curve model of replacement changes. In sum, by leveraging internet usage data, this first study of the life cycle of emergent neologisms has several contributions: A theory of how new words emerge, the correlation between emergent neologisms and emerging events, and the potential of modeling language use for epidemiological predictions.
KW - Emergent neologisms
KW - Language use model of epidemic
KW - Neologism formation strategies
KW - S-curve model of language change
UR - http://www.scopus.com/inward/record.url?scp=85107014302&partnerID=8YFLogxK
U2 - 10.1016/j.lingua.2021.103095
DO - 10.1016/j.lingua.2021.103095
M3 - Journal article
AN - SCOPUS:85107014302
SN - 0024-3841
VL - 258
JO - Lingua
JF - Lingua
M1 - 103095
ER -