Mining the vaccination willingness of China using social media data.
Int J Med Inform
; 170: 104941, 2023 02.
Article
in English
| MEDLINE | ID: covidwho-2229506
ABSTRACT
OBJECTIVE:
Vaccination is one of the most powerful and effective protective measures against Coronavirus disease 2019 (COVID-19). Currently, several blogs hold content on vaccination attitudes expressed on social media platforms, especially Sina Weibo, which is one of the largest social media platforms in China. Therefore, Weibo is a good data source for investigating public opinions about vaccination attitudes. In this paper, we aimed to effectively mine blogs to quantify the willingness of the public to get the COVID-19 vaccine. MATERIALS ANDMETHODS:
First, data including 144,379 Chinese blogs from Weibo, were collected between March 24 and April 28, 2021. The data were cleaned and preprocessed to ensure the quality of the experimental data, thereby reducing it to an experimental dataset of 72,496 blogs. Second, we employed a new fusion sentiment analysis model to analyze the sentiments of each blog. Third, the public's willingness to get the COVID-19 vaccine was quantified using the organic fusion of sentiment distribution and information dissemination effect.RESULTS:
(1) The intensity of bloggers' sentiment toward COVID-19 vaccines changed over time. (2) The extremum of positive and negative sentiment intensities occurred when hot topics related to vaccines appeared. (3) The study revealed that the public's willingness to get the COVID-19 vaccine and the actual vaccination doses shares a linear relationship.CONCLUSION:
We proposed a method for quantifying the public's vaccination willingness from social media data. The effectiveness of the method was demonstrated by a significant consistency between the estimates of public vaccination willingness and actual COVID-19 vaccination doses.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Social Media
/
COVID-19
Topics:
Vaccines
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Int J Med Inform
Journal subject:
Medical Informatics
Year:
2023
Document Type:
Article
Affiliation country:
J.ijmedinf.2022.104941
Similar
MEDLINE
...
LILACS
LIS