COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter
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 smal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Vaccines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/9/10/1059 |
| _version_ | 1797513071080505344 |
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| author | Amir Karami Michael Zhu Bailey Goldschmidt Hannah R. Boyajieff Mahdi M. Najafabadi |
| author_facet | Amir Karami Michael Zhu Bailey Goldschmidt Hannah R. Boyajieff Mahdi M. Najafabadi |
| author_sort | Amir Karami |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:09:33Z |
| format | Article |
| id | doaj.art-84730cf8fb3148d3932e7083773b0f62 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-10T06:09:33Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-84730cf8fb3148d3932e7083773b0f622023-11-22T20:14:28ZengMDPI AGVaccines2076-393X2021-09-01910105910.3390/vaccines9101059COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on TwitterAmir Karami0Michael Zhu1Bailey Goldschmidt2Hannah R. Boyajieff3Mahdi M. Najafabadi4School of Information Science, University of South Carolina, Columbia, SC 29208, USADepartment of Psychology, University of South Carolina, Columbia, SC 29208, USACollege of Nursing, University of South Carolina, Columbia, SC 29208, USADarla Moore School of Business, University of South Carolina, Columbia, SC 29208, USAGraduate School of Public Health, City University of New York, New York, NY 10010, USAThe 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.https://www.mdpi.com/2076-393X/9/10/1059COVID-19vaccinesocial mediatext miningtopic modelingsentiment analysis |
| spellingShingle | Amir Karami Michael Zhu Bailey Goldschmidt Hannah R. Boyajieff Mahdi M. Najafabadi COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter Vaccines COVID-19 vaccine social media text mining topic modeling sentiment analysis |
| title | COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter |
| title_full | COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter |
| title_fullStr | COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter |
| title_full_unstemmed | COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter |
| title_short | COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter |
| title_sort | covid 19 vaccine and social media in the u s exploring emotions and discussions on twitter |
| topic | COVID-19 vaccine social media text mining topic modeling sentiment analysis |
| url | https://www.mdpi.com/2076-393X/9/10/1059 |
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