# Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence

arXiv: https://arxiv.org/abs/2108.13293 · HTML: https://mlsanity.com/paper/2108.13293/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2021.

## Built on by (most cited first, 18 of 18)

- [Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study](https://mlsanity.com/paper/2211.15407/) (2022)
- [Vaccine Discourse on Twitter During the COVID-19 Pandemic](https://mlsanity.com/paper/2207.11521/) (2022)
- [Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data](https://mlsanity.com/paper/2401.15479/) (2024)
- [Detecting Conspiracy Theory Against COVID-19 Vaccines](https://mlsanity.com/paper/2211.13003/) (2022)
- [Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis](https://mlsanity.com/paper/2303.16759/) (2023)
- [Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict](https://mlsanity.com/paper/2301.08347/) (2023)
- [A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles](https://mlsanity.com/paper/2406.07693/) (2024)
- [Unveiling the Potential of BERTopic for Multilingual Fake News Analysis -- Use Case: Covid-19](https://mlsanity.com/paper/2407.08417/) (2024)
- [Digital Gatekeepers: Google's Role in Curating Hashtags and Subreddits](https://mlsanity.com/paper/2506.14370/) (2025)
- [Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem](https://mlsanity.com/paper/2604.18586/) (2026)
- [Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the Philippines](https://mlsanity.com/paper/2111.04416/) (2021)
- [The Effects of Political Martyrdom on Election Results: The Assassination of Abe](https://mlsanity.com/paper/2305.18004/) (2023)
- [Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset](https://mlsanity.com/paper/2401.13133/) (2024)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)
- [Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict](https://mlsanity.com/paper/2412.10913/) (2024)
- [VaxPulse: Monitoring of Online Public Concerns to Enhance Post-licensure Vaccine Surveillance](https://mlsanity.com/paper/2507.04656/) (2025)
- [Are Online Sports Fan Communities Becoming More Offensive? A Quantitative Review of Topics, Trends, and Toxicity of r/PremierLeague](https://mlsanity.com/paper/2510.27003/) (2025)
- [Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token](https://mlsanity.com/paper/2605.20192/) (2026)

## Abstract (arXiv)

The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. ty analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.