# Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study

arXiv: https://arxiv.org/abs/2211.15407 · HTML: https://mlsanity.com/paper/2211.15407/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence](https://mlsanity.com/paper/2108.13293/) (2021)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries](https://mlsanity.com/paper/2206.03286/) (2022)

## Built on by (most cited first, 4 of 4)

- [Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis](https://mlsanity.com/paper/2303.16759/) (2023)
- [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)
- [Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review](https://mlsanity.com/paper/2503.18095/) (2025)
- [Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights](https://mlsanity.com/paper/2505.07430/) (2025)

## Abstract (arXiv)

This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter, harvested from January 1, 2020, to March 1, 2022. To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict sentiments of approximately 9.5 million Tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 Tweets and then augmented our dataset by the method of back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python and the Huggingface sentiment analysis pipeline. Our results determined that the average sentiment expressed on Twitter was more negative (52% positive) than positive and the sentiment expressed on Reddit was more positive than negative (53% positive). Though average sentiment was found to vary between these social media platforms, both displayed similar behavior related to sentiment shared at key vaccine-related developments during the pandemic. Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can utilize to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety, fear, etc.), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to the population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.