| Literature DB >> 33151892 |
Runxi Zeng1, Menghan Li1.
Abstract
BACKGROUND: In recent years, public health incidents that pose a serious threat to public life have occurred frequently in China. The use of social media by public health authorities has helped to reduce these threats by increasing effective risk communication between the government and the public.Entities:
Keywords: COVID-19; Center for Disease Control and Prevention; China; government Weibo; public health agencies; social media
Mesh:
Year: 2020 PMID: 33151892 PMCID: PMC7744262 DOI: 10.2196/19470
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Figure 1Sina Weibo webpage of a Center for Disease Control and Prevention facility in China.
Figure 2Distribution of the Center for Disease Control and Prevention facilities in mainland China.
Distribution of CDC facilities and the CDC’s government Weibo accounts in mainland China.
| Province | CDCa, n | Government Weibo accounts, n (%) | Registration rate for each location (%) | |
| Total | 450 | 134 (100) | 29.8 | |
|
| 158 | 68 (50.7) | 43.0 | |
|
| Beijing | 17 | 17 (12.7) | 100.0 |
|
| Tianjin | 17 | 7 (5.2) | 41.2 |
|
| Hebei | 12 | 3 (2.2) | 25.0 |
|
| Liaoning | 15 | 11 (8.2) | 73.3 |
|
| Shanghai | 17 | 5 (3.7) | 29.4 |
|
| Jiangsu | 14 | 6 (4.5) | 42.9 |
|
| Zhejiang | 12 | 8 (6.0) | 66.7 |
|
| Shandong | 17 | 6 (4.5) | 35.3 |
|
| Guangdong | 22 | 4 (3.0) | 18.2 |
|
| Fujian | 10 | 1 (0.7) | 10.0 |
|
| Hainan | 5 | 0 (0) | 0 |
|
| 112 | 30 (22.4) | 26.8 | |
|
| Hubei | 14 | 4 (3.0) | 28.6 |
|
| Hunan | 15 | 5 (3.7) | 33.3 |
|
| Henan | 18 | 10 (7.6) | 55.6 |
|
| Anhui | 17 | 5 (3.7) | 29.4 |
|
| Jiangxi | 12 | 1 (0.7) | 8.3 |
|
| Shanxi | 12 | 4 (3.0) | 33.3 |
|
| Jilin | 10 | 1 (0.7) | 10.0 |
|
| Heilongjiang | 14 | 0 (0) | 0 |
|
| 180 | 36 (26.9) | 20.0 | |
|
| Guangxi | 15 | 2 (1.5) | 13.3 |
|
| Chongqing | 39 | 3 (2.2) | 7.7 |
|
| Sichuan | 22 | 8 (6.0) | 36.4 |
|
| Guizhou | 10 | 2 (1.5) | 20.0 |
|
| Inner Mongolia | 13 | 5 (3.8) | 38.5 |
|
| Yunnan | 17 | 3 (2.2) | 17.6 |
|
| Tibet | 8 | 0 (0) | 0 |
|
| Shaanxi | 11 | 2 (1.5) | 18.2 |
|
| Gansu | 15 | 5 (3.8) | 33.3 |
|
| Qinghai | 9 | 0 (0) | 0 |
|
| Ningxia | 6 | 3 (2.2) | 50.0 |
|
| Xinjiang | 15 | 3 (2.2) | 20.0 |
aCDC: Center for Disease Control and Prevention.
Figure 3Diffusion of government Weibo accounts (2009-2011).
Figure 4Diffusion of government Weibo accounts (2012-2014).
Figure 5Diffusion of government Weibo accounts (2015-2017).
Figure 6Diffusion of government Weibo accounts (2018-2019).
The update time of the most recent Weibo tweets from the Center for Disease Control and Prevention’s government accounts (n=134).
| Last update time (days) | Eastern region, n (%) | Central region, n (%) | Western region, n (%) | Total, n (%) |
| ≤30 | 27 (20.1) | 12 (9.0) | 11 (8.2) | 50 (37.3) |
| 31-90 | 6 (4.5) | 1 (0.7) | 3 (2.2) | 10 (7.5) |
| 91-365 | 13 (9.7) | 5 (3.7) | 4 (3.0) | 22 (16.4) |
| >365 | 21 (15.7) | 11 (8.2) | 15 (11.2) | 47 (35.1) |
| No content | 1 (0.7) | 1 (0.7) | 3 (2.2) | 5 (3.7) |
Descriptive statistical analysis results based on the topic type of tweets.
| Topic type | Disease control information | Emergency information | Popularization of health knowledge | Popularization of disease knowledge | Radiation hygiene/school hygiene | Government affairs trends | Policy interpretation | Weibo help/citizen consultation | Other |
| Tweets (n=1215), n (%) | 187 (15.4) | 15 (1.2) | 606 (49.9) | 137 (11.3) | 24 (2.0) | 95 (7.8) | 7 (0.6) | 14 (1.2) | 130 (10.7) |
| Number of retweets, average | 0.4 | 4.1 | 0.6 | 0.7 | 1.3 | 0.2 | 0 | 1.2 | 0.3 |
| Number of comments, average | 1.0 | 2.9 | 0.4 | 0.3 | 0.3 | 0.4 | 0 | 1.6 | 0.3 |
| Number of likes, average | 0.7 | 4.0 | 0.3 | 0.5 | 0.9 | 0.6 | 0.9 | 0.4 | 0.6 |