Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA.
Grossman School of Medicine, Department of Medicine, Division of Infectious Diseases and Immunology, New York University, New York, New York, USA.
Clin Infect Dis. 2022 May 15;74(Suppl_3):e4-e9. doi: 10.1093/cid/ciac141.
Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns.
A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States.
The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates.
Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.
接种疫苗有助于控制 2019 年冠状病毒病（COVID-19）大流行，但疫苗犹豫削弱了其效果。社交媒体传播有关疫苗接种的信息和错误信息。跟踪和分析社交媒体疫苗情绪可以让卫生专业人员更好地为疫苗接种对话和活动做准备。
使用自然语言处理情感分析（一种人工智能形式）开发了一个实时大数据分析框架。该框架实时摄取、处理和分析推文的情感和内容主题，例如自然健康或个人自由。后来的数据集评估了美国推特情绪评分与疫苗接种率之间的关系。
实时分析框架显示，随着疫苗推出后情绪差距扩大，情绪更加消极。推出疫苗后，使用静态数据集，积极情绪的增加伴随着疫苗接种的增加。对美国各地区的滞后交叉相关分析显示，一旦所有成年人都有资格接种疫苗，情绪评分与疫苗接种率之间存在一致的相关性，滞后约 1 周。格兰杰因果关系检验进一步表明，推文情绪评分可能有助于预测疫苗接种率。
社交媒体通过有价值的信息、错误信息和不信任影响了 COVID-19 的应对措施。该工具用于实时大规模收集和分析推文，以研究情绪和感兴趣的关键术语。单独的推文分析表明，疫苗接种率与推特疫苗情绪在区域上相关，可能预测疫苗接种率的变化，或指导有针对性的社交媒体和疫苗接种策略。需要进一步工作来分析特定人群、疫苗情绪和疫苗接种率之间的相互作用。
