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

• URL: http://arxiv.org/abs/2108.04816v1
• Date: Thu, 29 Jul 2021 17:31:11 GMT
• Title: COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter
• Authors: Amir Karami, Michael Zhu, Bailey Goldschmidt, Hannah R. Boyajieff, Mahdi M. Najafabadi
• Abstract summary: Public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. This study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets.
• Score: 9.834635805575582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 and their temporal trend, discovers major topics, compares topics of negative and non-negative tweets, and discloses top topics of negative and non-negative tweets. 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 negative and non-negative tweets regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses.

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