用社交媒体数据量化疫情事件对公众情绪的影响。
Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index
- 基于主成分分析构建多维度情感指数，动态捕捉情绪变化。
- 在450万条Reddit帖子中验证，关键事件前后情绪波动显著。
- 适合政策研究、公共卫生与舆情分析者参考。
本研究提出社区情绪与参与度指数（CSEI），用于捕捉社交媒体上公众情绪与参与度在新冠疫情重大事件中的细微变化。该指数整合了互动量、日发帖数、综合情感值、细粒度情绪（恐惧、惊讶、喜悦、悲伤、愤怒、厌恶和中性）、可读性、攻击性及领域多样性等特征。通过多步主成分分析（PCA）框架系统加权，根据各特征在时间序列情感波动中的方差贡献度调整权重，实现对敏感情绪变化的精准捕捉。开发过程显示CSEI与各组成特征具有统计学显著相关性，内部一致性高。使用包含4,510,178条关于新冠的Reddit帖子的数据集进行验证，聚焦15个重大事件，包括世卫组织宣布疫情为大流行、各国首例病例报告、国家封锁、疫苗研发进展及关键公共卫生措施。累计CSEI变化在这些事件前后呈现明显峰谷，揭示了疫情不同阶段的公众情绪模式。皮尔逊相关性分析进一步证实，CSEI的日度波动与这些事件存在统计显著关联（p = 0.0428），表明该指数具备推断并解读公众情绪与参与度响应重大疫情事件的能力。
原文摘要 · Abstract (English)
This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. The development of CSEI showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, national lockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting the capacity of CSEI to infer and interpret shifts in public sentiment and engagement in response to major events related to COVID-19.
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