# Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index

arXiv: https://arxiv.org/abs/2412.16925 · HTML: https://mlsanity.com/paper/2412.16925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 92 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Eigenvalue and Generalized Eigenvalue Problems: Tutorial](https://mlsanity.com/paper/1903.11240/) (2019)
- [A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave](https://mlsanity.com/paper/2208.07810/) (2022)
- [Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox](https://mlsanity.com/paper/2312.10580/) (2023)
- [An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection](https://mlsanity.com/paper/2208.10252/) (2022)
- [Exploration of Gender Differences in COVID-19 Discourse on Reddit](https://mlsanity.com/paper/2008.05713/) (2020)
- [Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit](https://mlsanity.com/paper/2005.10454/) (2020)
- [Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story](https://mlsanity.com/paper/2101.06321/) (2021)
- [Reddit in the Time of COVID](https://mlsanity.com/paper/2304.10777/) (2023)
- [COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study](https://mlsanity.com/paper/2011.10518/) (2020)
- [Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina](https://mlsanity.com/paper/2106.04515/) (2021)

## Built on by (most cited first, 1 of 1)

- [Content and Engagement Trends in COVID-19 YouTube Videos: Evidence from the Late Pandemic](https://mlsanity.com/paper/2509.01954/) (2025)

## Abstract (arXiv)

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.