# COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter

arXiv: https://arxiv.org/abs/2108.04816 · HTML: https://mlsanity.com/paper/2108.04816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Fuzzy Approach Topic Discovery in Health and Medical Corpora](https://mlsanity.com/paper/1705.00995/) (2017)
- [FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents](https://mlsanity.com/paper/1911.10953/) (2019)
- [Social Media and COVID-19: Can Social Distancing be Quantified without Measuring Human Movements?](https://mlsanity.com/paper/2006.05316/) (2020)

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

- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)

## Abstract (arXiv)

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.