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.

翻译：本研究引入了社区情感与参与度指数（CSEI），旨在捕捉社交媒体上公众情感与参与度的细微变化，特别是在应对与COVID-19相关的重大事件时。CSEI通过多样化的情感指标构建，整合了参与度、每日发帖量、复合情感、细粒度情感（恐惧、惊讶、喜悦、悲伤、愤怒、厌恶及中性）、可读性、攻击性及领域多样性等特征。各组成部分通过一个基于多步骤主成分分析（PCA）的框架进行系统加权，依据其在时序情感变化中的方差贡献对特征进行优先级排序。该方法动态调整各成分的重要性，使CSEI能够精确捕捉公众情感的高敏感性波动。CSEI的构建过程显示其与各构成特征之间存在统计学上显著的相关性，强调了其内部一致性及对特定情感维度的敏感性。CSEI的响应能力通过一个包含4,510,178条关于COVID-19的Reddit帖子的数据集得到验证。分析聚焦于15个重大事件，包括世界卫生组织宣布COVID-19为大流行病、各国首次报告COVID-19病例、全国性封锁、疫苗研发以及关键的公共卫生措施。CSEI的累积变化显示出与这些事件相对应的显著波峰与波谷，揭示了疫情不同阶段公众情感的显著模式。皮尔逊相关性分析进一步证实了CSEI的日度波动与这些事件之间存在统计学上显著的关系（p = 0.0428），凸显了CSEI在推断和解读公众针对COVID-19相关重大事件的情感与参与度变化方面的能力。