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Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

BACKGROUND: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although an...

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Autores principales: Honcharov, Vlad, Li, Jiawei, Sierra, Maribel, Rivadeneira, Natalie A, Olazo, Kristan, Nguyen, Thu T, Mackey, Tim K, Sarkar, Urmimala
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039410/
https://www.ncbi.nlm.nih.gov/pubmed/37113377
http://dx.doi.org/10.2196/40575
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author Honcharov, Vlad
Li, Jiawei
Sierra, Maribel
Rivadeneira, Natalie A
Olazo, Kristan
Nguyen, Thu T
Mackey, Tim K
Sarkar, Urmimala
author_facet Honcharov, Vlad
Li, Jiawei
Sierra, Maribel
Rivadeneira, Natalie A
Olazo, Kristan
Nguyen, Thu T
Mackey, Tim K
Sarkar, Urmimala
author_sort Honcharov, Vlad
collection PubMed
description BACKGROUND: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. OBJECTIVE: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. METHODS: We used a data set of COVID-19–related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags “antivaxxing,” “antivaxx,” “antivaxxers,” “antivax,” “anti-vaxxer,” “discredit,” “undermine,” “confidence,” and “immune.” Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. RESULTS: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using “anti-vax” as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. CONCLUSIONS: Most discussions surrounding public figures in common hashtags labelled as “anti-vax” did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax–related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.
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spelling pubmed-100394102023-04-26 Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis Honcharov, Vlad Li, Jiawei Sierra, Maribel Rivadeneira, Natalie A Olazo, Kristan Nguyen, Thu T Mackey, Tim K Sarkar, Urmimala JMIR Infodemiology Original Paper BACKGROUND: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. OBJECTIVE: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. METHODS: We used a data set of COVID-19–related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags “antivaxxing,” “antivaxx,” “antivaxxers,” “antivax,” “anti-vaxxer,” “discredit,” “undermine,” “confidence,” and “immune.” Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. RESULTS: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using “anti-vax” as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. CONCLUSIONS: Most discussions surrounding public figures in common hashtags labelled as “anti-vax” did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax–related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse. JMIR Publications 2023-03-10 /pmc/articles/PMC10039410/ /pubmed/37113377 http://dx.doi.org/10.2196/40575 Text en ©Vlad Honcharov, Jiawei Li, Maribel Sierra, Natalie A Rivadeneira, Kristan Olazo, Thu T Nguyen, Tim K Mackey, Urmimala Sarkar. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 10.03.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Honcharov, Vlad
Li, Jiawei
Sierra, Maribel
Rivadeneira, Natalie A
Olazo, Kristan
Nguyen, Thu T
Mackey, Tim K
Sarkar, Urmimala
Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title_full Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title_fullStr Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title_full_unstemmed Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title_short Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
title_sort public figure vaccination rhetoric and vaccine hesitancy: retrospective twitter analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039410/
https://www.ncbi.nlm.nih.gov/pubmed/37113377
http://dx.doi.org/10.2196/40575
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