Forecasting COVID-19 Vaccination Rates using Social Media Data
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
; : 1020-1029, 2023.
Article
in English
| Scopus | ID: covidwho-20238654
ABSTRACT
The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.
COVID-19; text classification; tweet analysis; vaccination intent; vaccination rate forecasting; Classification (of information); Forecasting; Regression analysis; Social networking (online); Text processing; Time series analysis; Vaccines; Classification approach; Gain insight; Global community; Public perception; Social media datum; Social media platforms; Tweet analyse
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Vaccines
Language:
English
Journal:
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Year:
2023
Document Type:
Article
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