Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019.
Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach an...
| Published in: | Pan American Journal of Public Health Vol. 45; pp. 1 - 8 |
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| Main Authors: | , , , , , |
| Format: | Article |
| Published: |
World Health Organization
2021
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=153765287&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 153765287 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10204989 55OA jtl: Pan American Journal of Public Health issn: 10204989 maglogo: N pubinfo: dt: 2021 vid: 45 pid: 437 pub: World Health Organization artinfo: ui: 153765287 10.26633/RPSP.2021.54 ppf: 1 ppct: 7 formats: tig: atl: Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019. aug: au: Bonnevie, Erika Goldbarg, Jaclyn Gallegos-Jeffry, Allison K. Rosenberg, Sarah D. Wartella, Ellen Smyser, Joe affil: The Public Good Projects, Alexandria, Estados Unidos de América Northwestern School of Communication, Evanston, Estados Unidos de América su: X Corp. Public health surveillance Information resources Supervised learning Computer programming education Acquisition of data sug: subj: Public health surveillance Information resources X Corp. Custom Computer Programming Services Other Computer Related Services Computer systems design and related services (except video game design and development) Supervised learning Computer programming education Acquisition of data keyword: Información redes sociales salud pública vacunas Información redes sociales salud pública vacunas ab: Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition. Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes. Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in counter-messaging and public health surveillance. pubtype: Academic Journal doctype: Article src: R language: Spanish refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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