Revealing Public Opinion towards the Covid-19 Vaccine with Weibo Data in China

BertFDA-Based Model

Datos Bibliográficos

ID15514420
AutoresJianping Zhu (0009-0002-1453-0603, Xiamen University), Futian Weng (0000-0002-7982-8729, Xiamen University, autor de correspondencia), Muni Zhuang (0000-0002-8239-0000, Xiamen University, autor de correspondencia), Xin Lu (0000-0001-6381-3672, National University of Defense Technology), Xu Tan (0000-0003-4861-4573, Shenzhen Institute of Information Technology), Songjie Lin (Shenzhen Institute of Information Technology), Ruoyi Zhang (0000-0002-7941-2969, George Washington University)
Año2022
Volumen19
Número20
Páginas13248-13248
Fecha de publicación2022-10-14
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores de la revistaISSN: 1661-7827 • E-ISSN: 1660-4601
EditorialMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192013248
PMID36293828
OpenAlexW4306377238
IdiomaEN
Citas recibidas3
Referencias citadas48

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates

Coronavirus disease 2019 (COVID-19 · Data science · Latent Dirichlet allocation · Microblogging · Pandemic · Political science · Public health · Public opinion · Sentiment analysis · Social distance · Social media · Topic model · World Wide Web · Computer Science · Influenza Virus Research Studies · Medicine · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Artificial Intelligence