Forecasting COVID-19 Vaccination Rates using Social Media Data
Forecasting COVID-19 Vaccination Rates using Social Media Data
复制标题
使用社交媒体数据预测 COVID-19 疫苗接种率
DOI:
10.1145/3543873.3587639
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Culotta, Aron
中科院分区:
文献类型:
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作者:
Li, Xintian;Culotta, Aron
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.
DOI:
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发表时间:
2021
期刊:
arXiv.org
影响因子:
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作者:
Francesco Pierri;B. Perry;Matthew R. Deverna;Kai;A. Flammini;F. Menczer;J. Bryden
通讯作者:
J. Bryden
DOI:
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发表时间:
--
期刊:
影响因子:
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作者:
通讯作者:
--
DOI:
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发表时间:
2021
期刊:
ICWSM Workshops
影响因子:
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作者:
Marco Di Giovanni;L. Corti;S. Pavanetto;Francesco Pierri;Andrea Tocchetti;Marco Brambilla
通讯作者:
Marco Brambilla