Forecasting COVID-19 Vaccination Rates using Social Media Data

Forecasting COVID-19 Vaccination Rates using Social Media Data
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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:
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
影响因子:
--
通讯作者:
Culotta, Aron
Culotta, Aron
中科院分区:
--
文献类型:
--
作者:
Li, Xintian;Culotta, Aron

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新冠肺炎大流行对全球社会产生了深刻影响,接种疫苗已被认为是一项关键的干预措施。为了了解公众对新冠肺炎疫苗的看法,我们进行了调查研究和社交媒体平台的分析。然而,现有的方法缺乏对个人接种意图或状况的考虑,以及公众感知和实际疫苗接种量之间的关系。为了解决这些局限性,本研究提出了一种文本分类方法来识别表示用户对疫苗接种的意图或状态的推文。来自不同类别的推文比例与真实世界的疫苗接种数据之间的对比分析显示出显著的一致性,这表明推文可能是实际疫苗接种状态的先兆。此外,还进行了回归分析和时间序列预测,以探索推特数据的潜力,论证了纳入推特数据在预测未来疫苗接种状况方面的重要性。最后,对具有正面和负面标签的推文集进行聚类,以深入了解每种姿态的潜在焦点。
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.
在线错误信息对美国 COVID-19 疫苗接种的影响
DOI: --
发表时间: 2021
期刊: arXiv.org
影响因子: --
作者:
Francesco Pierri;B. Perry;Matthew R. Deverna;Kai;A. Flammini;F. Menczer;J. Bryden
通讯作者: J. Bryden
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DOI: --
发表时间: --
期刊:
影响因子: --
作者:
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Twitter 上疫苗相关立场的基于内容的分析和分类方法:意大利情景
DOI: --
发表时间: 2021
期刊: ICWSM Workshops
影响因子: --
作者:
Marco Di Giovanni;L. Corti;S. Pavanetto;Francesco Pierri;Andrea Tocchetti;Marco Brambilla
通讯作者: Marco Brambilla