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Public Perception of COVID-19 Vaccines on Twitter in the United States
BACKGROUND: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. OBJECTIVE: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547532/ https://www.ncbi.nlm.nih.gov/pubmed/34704100 http://dx.doi.org/10.1101/2021.10.16.21265097 |
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author | Xie, Zidian Wang, Xueting Jiang, Yan Chen, Yuhan Huang, Shengyuan Ma, Haoxuan Anand, Ajay Li, Dongmei |
author_facet | Xie, Zidian Wang, Xueting Jiang, Yan Chen, Yuhan Huang, Shengyuan Ma, Haoxuan Anand, Ajay Li, Dongmei |
author_sort | Xie, Zidian |
collection | PubMed |
description | BACKGROUND: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. OBJECTIVE: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. METHODS: Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5 (th) , 2020 to January 25 (th) , 2021 using relevant keywords (such as “corona”, “covid19”, and “covid”). Based on geolocation information provided in tweets and vaccine-related keywords (such as “vaccine” and “vaccination”), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. RESULTS: Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. CONCLUSIONS: Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users. |
format | Online Article Text |
id | pubmed-8547532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-85475322021-10-27 Public Perception of COVID-19 Vaccines on Twitter in the United States Xie, Zidian Wang, Xueting Jiang, Yan Chen, Yuhan Huang, Shengyuan Ma, Haoxuan Anand, Ajay Li, Dongmei medRxiv Article BACKGROUND: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. OBJECTIVE: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. METHODS: Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5 (th) , 2020 to January 25 (th) , 2021 using relevant keywords (such as “corona”, “covid19”, and “covid”). Based on geolocation information provided in tweets and vaccine-related keywords (such as “vaccine” and “vaccination”), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. RESULTS: Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. CONCLUSIONS: Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users. Cold Spring Harbor Laboratory 2021-10-18 /pmc/articles/PMC8547532/ /pubmed/34704100 http://dx.doi.org/10.1101/2021.10.16.21265097 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Xie, Zidian Wang, Xueting Jiang, Yan Chen, Yuhan Huang, Shengyuan Ma, Haoxuan Anand, Ajay Li, Dongmei Public Perception of COVID-19 Vaccines on Twitter in the United States |
title |
Public Perception of COVID-19 Vaccines on Twitter in the United States
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title_full |
Public Perception of COVID-19 Vaccines on Twitter in the United States
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title_fullStr |
Public Perception of COVID-19 Vaccines on Twitter in the United States
|
title_full_unstemmed |
Public Perception of COVID-19 Vaccines on Twitter in the United States
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title_short |
Public Perception of COVID-19 Vaccines on Twitter in the United States
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title_sort | public perception of covid-19 vaccines on twitter in the united states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547532/ https://www.ncbi.nlm.nih.gov/pubmed/34704100 http://dx.doi.org/10.1101/2021.10.16.21265097 |
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