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Table 1 Variable definitions and relationship hypotheses

From: Netizens’ risk perception in new coronary pneumonia public health events: an analysis of spatiotemporal distribution and influencing factors

Variable types and names Definition of variables (quantitative units or symbols) Relationship Hypothesesa
Explained variables:
 Internet search volume density Yi (i.e., RIi) The proportion of information demand of Internet users in each province to the total information demand of Internet users nationwide (%)  
Explanatory variables:
 Male to female sex ratio (X1) Male share of total population (%) Higher risk perception among men (+)
 Age structure (X2) Population aged 15–64 as a percentage of total population (%)b The higher the ratio, the higher the risk perception (+)
 Years of education per capita (X3) Years of formal education only (Years) The longer the number of years of education, the higher the risk perception (+)
 Mortality (X4) Ratio of the number of deaths by province in a year to the average number for the same period (%) The higher the mortality rate (lower the regional health level), the lower the risk perception (−)
 Per capita GDP (X5) Final results of production activities of all resident units in the provinces during the year (billion yuan, RMB) Higher risk perception in provinces with higher GDP (economically developed areas)
 Rate of decline in risk perception (X6) (λ value) The probability of Internet users searching for the COVID-19 event reflects the extent of Internet information disclosure The higher the search volume, i.e., the faster the rate of decline, the higher the risk perception (+)
  1. aIn the relationship hypotheses, “+” indicates the explanatory variables have a positive correlation with the explained variables, and “-” indicates a negative correlation
  2. bThis paper defines Internet users aged 15–64 as Internet users with sanity. According to this definition, the search volume of Internet users collected in this paper may have a small systemic error