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arXiv · 2108.04816

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

Amir Karami·Michael Zhu·Bailey Goldschmidt·Hannah R. Boyajieff·Mahdi M. Najafabadi
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Abstract

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.

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Amir Karami, Michael Zhu, Bailey Goldschmidt, Hannah R. Boyajieff, Mahdi M. Najafabadi. 2021-07-29. COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter. https://doi.org/10.3390/vaccines9101059

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