Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
BackgroundSocial 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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-03-01
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| Series: | JMIR Infodemiology |
| Online Access: | https://infodemiology.jmir.org/2023/1/e40575 |
| _version_ | 1797734345464610816 |
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| author | Vlad Honcharov Jiawei Li Maribel Sierra Natalie A Rivadeneira Kristan Olazo Thu T Nguyen Tim K Mackey Urmimala Sarkar |
| author_facet | Vlad Honcharov Jiawei Li Maribel Sierra Natalie A Rivadeneira Kristan Olazo Thu T Nguyen Tim K Mackey Urmimala Sarkar |
| author_sort | Vlad Honcharov |
| collection | DOAJ |
| description |
BackgroundSocial 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.
ObjectiveWe 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.
MethodsWe 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.
ResultsOur 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.
ConclusionsMost 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. |
| first_indexed | 2024-03-12T12:42:55Z |
| format | Article |
| id | doaj.art-fba20bee06e44477893776936de76114 |
| institution | Directory Open Access Journal |
| issn | 2564-1891 |
| language | English |
| last_indexed | 2024-03-12T12:42:55Z |
| publishDate | 2023-03-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Infodemiology |
| spelling | doaj.art-fba20bee06e44477893776936de761142023-08-28T23:44:45ZengJMIR PublicationsJMIR Infodemiology2564-18912023-03-013e4057510.2196/40575Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter AnalysisVlad Honcharovhttps://orcid.org/0000-0002-6045-6515Jiawei Lihttps://orcid.org/0000-0001-9801-4715Maribel Sierrahttps://orcid.org/0000-0002-3390-9961Natalie A Rivadeneirahttps://orcid.org/0000-0003-2421-9915Kristan Olazohttps://orcid.org/0000-0003-2122-2393Thu T Nguyenhttps://orcid.org/0000-0003-1185-045XTim K Mackeyhttps://orcid.org/0000-0002-2191-7833Urmimala Sarkarhttps://orcid.org/0000-0003-4213-4405 BackgroundSocial 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. ObjectiveWe 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. MethodsWe 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. ResultsOur 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. ConclusionsMost 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.https://infodemiology.jmir.org/2023/1/e40575 |
| spellingShingle | Vlad Honcharov Jiawei Li Maribel Sierra Natalie A Rivadeneira Kristan Olazo Thu T Nguyen Tim K Mackey Urmimala Sarkar Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis JMIR Infodemiology |
| 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 |
| url | https://infodemiology.jmir.org/2023/1/e40575 |
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