Background
Theoretical foundation
Methods
Analysis framework
Analysis of temporal distribution
Analysis of spatial distribution
Analysis of space-time interaction
Variables & samples
Variable definitions and relationship hypotheses
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 (+) |
Sample selection
Empirical analysis model
Data description and statistical results
Data source
Data description
Description of the temporal evolution of internet users’ risk perceptions
Description of the spatial distribution of internet users’ risk perception
Observation time | Internet users network search volume density/province name | Descriptive statistics results | |||
---|---|---|---|---|---|
Max | Min | Mean | Range | Standard deviation | |
Total national two-month period (8 weeks in total) | |||||
Internet search volume density (RIi) | Beijing | Xinjiang | 5.2 | 8.83 | 1.71 |
7.84% | 1.98% | ||||
Number of new confirmed cases (persons) | Guangdong | Qinghai | 431.62 | 1278 | 392.76 |
1295 | 17 | ||||
The day after the outbreak (Jan 23rd) | |||||
Internet search volume density (RIi) | Beijing | Guangdong | 0.1366 | 0.058 | 0.34 |
8.85% | 0.33% | ||||
Number of new confirmed cases (persons) | Chongqing | Shanxi, Zhejiang, Shandong, Guangdong, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Xinjiang | 3.65 | 18 | 4.85 |
18 | 0 | ||||
First week after the outbreak | |||||
Internet search volume density (RIi) | Beijing | Xinjiang | 1.31 | 2.11 | 0.38 |
7.41% | 1.86% | ||||
Number of new confirmed cases (persons) | Zhejiang | Qinghai | 119.76 | 487 | 120.45 |
494 | 7 | ||||
Week 2 after the outbreak | |||||
Internet search volume density (RIi) | Beijing | Xinjiang | 1 | 1.75 | 0.31 |
8.04% | 2.02% | ||||
Number of new confirmed cases (persons) | Guangdong | Qinghai | 176.07 | 615 | 174.52 |
625 | 10 | ||||
Week 5 after the outbreak | |||||
Internet search volume density (RIi) | Beijing | Guangxi | 0.43 | 0.78 | 0.16 |
8.44% | 2.21% | ||||
Number of new confirmed cases (persons) | Guangdong | Inner Mongolia, Liaoning, Jiangsu, Hainan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Xinjiang | 3.52 | 15 | 4.61 |
15 | 0 | ||||
Week 8 after the outbreak | |||||
Internet search volume density (RIi) | Beijing | Xinjiang | 0.23 | 0.51 | 0.11 |
9.34% | 1.83% | ||||
Number of new confirmed cases (persons) | Beijing | Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Jiangsu, Anhui, Fujian, Jiangxi, Henan, Hunan, Hainan, Chongqing, Guizhou, Qinghai, Ningxia, Xinjiang | 4.69 | 49 | 11.76 |
49 | 0 |
Jan 23 | Week 1 | Week 2 | Week 5 | Week 8 | |||||
---|---|---|---|---|---|---|---|---|---|
RIi value | New confirmed cases (persons) | Weekly RIi mean | New confirmed cases (persons) | Weekly RIi mean | New confirmed cases (persons) | Weekly RIi mean | New confirmed cases (persons) | Weekly RIi mean | New confirmed cases (persons) |
> = 7.5% Risk Perception Level I | |||||||||
Beijing | 12 | Beijing | 160 | Beijing | 14 | Beijing | 49 | ||
5% ~ 7.5% Risk Perception Level II | |||||||||
Shanghai | 4 | Beijing | 103 | Shanghai | 3 | Shanghai | 25 | ||
Zhejiang | 7 | ||||||||
2.5% ~ 5% Risk Perception Level III | |||||||||
Zhejiang | 0 | Zhejiang | 494 | Shanghai | 141 | Zhejiang | 2 | Guangdong | 37 |
Hainan | 4 | Shandong | 154 | Zhejiang | 469 | Guangdong | 15 | Tianjin | 1 |
Jiangsu | 8 | Liaoning | 41 | Tianjin | 48 | Chongqing | 9 | Hainan | 0 |
Chongqing | 18 | Shanghai | 108 | Shandong | 201 | Jiangsu | 0 | Jiangsu | 0 |
Shandong | 0 | Jiangsu | 159 | Liaoning | 39 | Tianjin | 5 | Chongqing | 0 |
Fujian | 3 | Hebei | 80 | Hebei | 89 | Shandong | 8 | Fujian | 0 |
Shaanxi | 0 | Chongqing | 179 | Jiangsu | 240 | Sichuan | 13 | Qinghai | 0 |
Sichuan | 10 | Tianjin | 26 | Jilin | 51 | Hebei | 5 | Liaoning | 0 |
Anhui | 6 | Anhui | 222 | Chongqing | 205 | Hainan | 0 | Shandong | 2 |
Hebei | 1 | Hainan | 41 | Ningxia | 22 | Fujian | 3 | Sichuan | 2 |
Tianjin | 1 | Sichuan | 163 | Qinghai | 10 | Liaoning | 0 | Hebei | 0 |
Jiangxi | 4 | Jiangxi | 233 | Sichuan | 167 | Jilin | 2 | Ningxia | 0 |
Liaoning | 2 | Qinghai | 7 | Hainan | 62 | Ningxia | 1 | Jilin | 0 |
Hunan | 15 | Jilin | 11 | Anhui | 428 | Qinghai | 0 | Guizhou | 0 |
Shanxi | 0 | Fujian | 114 | InnerMongolia | 28 | Anhui | 2 | Shaanxi | 1 |
Henan | 4 | InnerMongoli | 14 | Guangdong | 625 | InnerMongolia | 0 | Jiangxi | 0 |
Qinghai | 0 | Guangdong | 295 | Jiangxi | 423 | Gansu | 0 | Heilongjiang | 2 |
Heilongjiang | 2 | Shanxi | 38 | Guizhou | 62 | Shanxi | 1 | InnerMongolia | 0 |
Guizhou | 0 | Ningxia | 19 | Shanxi | 57 | Guizhou | 0 | Shanxi | 0 |
Gansu | 0 | Shaanxi | 60 | Shaanxi | 111 | Jiangxi | 1 | ||
InnerMongolia | 1 | Hunan | 308 | Fujian | 104 | Henan | 5 | ||
Jilin | 2 | Heilongjiang | 55 | Heilongjiang | 218 | Hunan | 6 | ||
Guizhou | 12 | Henan | 513 | Heilongjiang | 1 | ||||
Gansu | 27 | Hunan | 440 | Shaanxi | 0 | ||||
Gansu | 33 | ||||||||
0 ~ 2.5% Risk perception level IV | |||||||||
Xinjiang | 0 | Henan | 343 | Yunnan | 53 | Xinjiang | 0 | Henan | 0 |
Ningxia | 1 | Yunnan | 78 | Guangxi | 85 | Yunnan | 0 | Hunan | 0 |
Guangxi | 8 | Guangxi | 74 | Xinjiang | 22 | Guangxi | 0 | Guangxi | 1 |
Yunnan | 0 | Xinjiang | 15 | Gansu | 7 | ||||
Guangdong | 0 | Yunnan | 2 | ||||||
Anhui | 0 | ||||||||
Xinjiang | 0 |
Space-time description of the shift of “topic heat” in internet users’ risk perception
Topics | Week | Public opinion ranking | Total public opinions (million times) | Public opinion categories | Topics | Week | Public opinion ranking | Total public opinions (million times) | Public opinion categories | |
---|---|---|---|---|---|---|---|---|---|---|
Shuanghuanglian inhibits 2019-nCoV | 2 | 1 | 2220.555 | 1 | CCTV reporter visited the Wuhan Red Cross | 2 | 5 | 850.335 | 1 | |
Dr. Li Wenliang dies | 3 | 1 | 2011.394 | 1 | Large number of 2019-nCoV present in South China seafood market | 1 | 3 | 830.144 | 1 | |
Epidemiologist says epidemic cannot wait | 1 | 1 | 1640.293 | 2 | Zhang Wenhong predicts eventual development of new coronavirus | 6 | 2 | 820.116 | 2 | |
The epidemic is still spreading | 1 | 2 | 1590.255 | 1 | Experts advise teachers to wear masks after school starts | 5 | 1 | 810.076 | 2 | |
Many places clarify school start time | 8 | 1 | 1550.123 | 1 | Zhang Wenhong said the epidemic is basically impossible to end this summer | 8 | 2 | 800.085 | 2 | |
Hubei deputy governor responds to Wuhan residents’ online requests for help | 2 | 2 | 1440.396 | 1 | Children of front-line volunteers in Hubei added 10 points for admission to the high school entrance examination | 4 | 4 | 780.079 | 1 | |
New pneumonia help channel opens | 2 | 3 | 1304.721 | 1 | Zhong Nanshan talks about specific antiviral drugs | 2 | 6 | 770.06 | 2 | |
Wuhan will be held accountable for finding home-diagnosed patients | 4 | 1 | 1180.052 | 1 | Trump declares national emergency in response to epidemic | 8 | 3 | 750.051 | 3 | |
16 provinces a province package a city to support Hubei | 3 | 2 | 1120.192 | 1 | Trump praises China’s epidemic prevention and control achievements | 8 | 6 | 680.061 | 3 | |
Zhong Nanshan talks about the peak of the new crown pneumonia epidemic | 4 | 2 | 1020.082 | 2 | South Korea confirms for the first time that Xintiandi believers have been to Wuhan | 6 | 7 | 680.023 | 3 | |
Change in responsibilities of main comrade of Hubei provincial party | 3 | 3 | 970.113 | 1 | Zhong Nanshan says end of epidemic is to be expected in June | 7 | 3 | 590.024 | 2 | |
Italian residents refuse to wear masks | 6 | 1 | 960.099 | 3 | Italy’s mask supply goes missing after being withheld by Germany | 8 | 10 | 570.018 | 3 | |
The first person to report the epidemic is lauded by Hubei Provincial Department of Human Resources and Social Security and Hubei Provincial Health Commission. | 2 | 4 | 870.08 | 1 | Zhong Nanshan says foreign epidemic much like early Wuhan situation | 7 | 6 | 546.014 | 2 | |
Autopsy of the body of the first patient who died from COVID-19 in China | 4 | 3 | 860.077 | 1 | Congressional physicians predict nearly half of Americans could be infected | 7 | 9 | 520.014 | 3 |
Goodness of fit of internet search volume index model for internet users’ risk perception
Wuhan “city closing” day | Week 1 | Week 2 | Week 5 | Week 8 | |
---|---|---|---|---|---|
λ | 0.153 | 0.337 | 0.315 | 0.169 | 0.034 |
Adjusted R2 | 0.831 | 0.875 | 0.903 | 0.914 | 0.922 |
Linear regression analysis of factors influencing internet users’ risk perceptions
Variable | Equation I | Equation II | Equation III | Equation IV | Equation V |
---|---|---|---|---|---|
X1 | 0.00135 (0.613959) | 3.77E-02a (1.991287) | 0.006534 (0.83375) | 1.71E-03 (1.64111) | 0.001736 (1.86153) |
X2 | 0.060085 (1.486521) | 0.001965 (1.825641) | 7.66E-04 (1.852471) | 1.003657a (2.663358) | 0.904527 (1.375526) |
X3 | 8.257481b (2.307547) | 1.10E-06a (2.085241) | -0.82E-03 (−1.754117) | 0.066856a (2.15417) | 1.021367a (2.875502) |
X4 | -1.214574a (−1.985114) | -1.70063b (−2.425471) | -1.004772a (−2.036574) | −0.001845 (−1.00036) | 4.54E-05b (2.000067) |
X5 | 6.85E-02b (4.195523) | 3.345E-02c (5.145672) | 2.87E-05c (4.837169) | 1.66E-04b (2.105878) | 1.37E-05a (2.052741) |
X6 | 1.978421b (2.854632) | 1.003527c (3.745811) | 1.643589b (2.107952) | 0.000275 (1.795422) | 1.087235a (2.111253) |
Adjusted -squared | 0.685411 | 0.073251 | 0.453375 | 0.268742 | 0.204685 |
F-statistic | 10.852311c | 3.114785b | 2.728768a | 5.416758c | 3.000256b |