VACUNAS ANTICOVID Y TROMBOSIS: EL MIEDO EN LAS REDES SOCIALES.
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| Title: | VACUNAS ANTICOVID Y TROMBOSIS: EL MIEDO EN LAS REDES SOCIALES. |
|---|---|
| Alternate Title: | Anticovid vaccines and thrombosis: fear in the social networks. |
| Authors: | Arce-García, Sergio1 sergio.arce@unir.net, Cano-Garcinuño, María-Isabel2 icanog@hotmail.com, Quiles-Cano, Cristina3 cristina.quilescano@gmail.com, Cano-Pérez, Julia4 juliacanoperez2@gmail.com |
| Source: | Revista de Comunicación y Salud. 2024, Vol. 14, p1-19. 19p. |
| Subjects: | TEXT mining, SENTIMENT analysis, SOCIAL networks, MACHINE learning, DISINFORMATION |
| Abstract (English): | Background: This article aims to analyse the existing relationship in the Spanish social network Twitter between thrombi and anticovid vaccines. Methods: Machine learning techniques and massive statistical analysis were used to determine the social networks formed their relationships, sentiment and emotion analysis and main discourses by text mining during four months. For this purpose, 915,825 messages discussing thrombosis were collected along the first four months of 2021. Results: We found a very prominent increase in messages around mid-March and a sharp rise in early April, coinciding with detected cases and temporary suspensions of certain vaccines in the United States and Europe. The main of messages came from Spain, although Mexico focuses the debate on Latin America. Conclusions: It was possible to determine an increase in messages with a high emotional charge, mainly negative, as well as disinformative and conspiratorial messages, especially from groups without significant referents. The disseminators of disinformation news about vaccines are sent by small nano-influencers or fake accounts using possible astroturfing techniques. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | Fundamentos: El presente artículo tiene como objetivo analizar la relación existente en la red social Twitter en español entre trombos y vacunas anticovid. Métodos: Se utilizaron técnicas de machine learning y análisis estadístico masivo para la determinación de redes sociales formadas, sus relaciones, análisis de sentimientos y emociones y discursos principales por minería de texto durante cuatro meses. Para ello se recogieron 915.825 mensajes que hablaran de trombosis a lo largo de los cuatro primeros meses del año 2021. Resultados: Se encontró un aumento muy destacado de mensajes alrededor desde mediados de marzo e incrementándose fuertemente a principios de abril, coincidiendo con los casos detectados y suspensiones temporales de ciertas vacunas en Estados Unidos y Europa. El principal de mensajes procede de España, aunque México centra el debate en Latinoamérica. Conclusiones: Se pudieron determinar un aumento de mensajes de alta carga emocional, principalmente negativa, así como mensajes desinformativos y conspirativos especialmente desde unos grupos sin referentes significativos. Los difusores de noticias de desinformación sobre vacunas son enviados por pequeñas cuentas nano-influencers o cuentas falsas utilizando posibles técnicas astroturfing. [ABSTRACT FROM AUTHOR] |
| Copyright of Revista de Comunicación y Salud is the property of Instituto Internacional de Comunicacion y Salud and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: lth DbLabel: MedicLatina An: 181693952 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: VACUNAS ANTICOVID Y TROMBOSIS: EL MIEDO EN LAS REDES SOCIALES. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Anticovid vaccines and thrombosis: fear in the social networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arce-García%2C+Sergio%22">Arce-García, Sergio</searchLink><relatesTo>1</relatesTo><i> sergio.arce@unir.net</i><br /><searchLink fieldCode="AR" term="%22Cano-Garcinuño%2C+María-Isabel%22">Cano-Garcinuño, María-Isabel</searchLink><relatesTo>2</relatesTo><i> icanog@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Quiles-Cano%2C+Cristina%22">Quiles-Cano, Cristina</searchLink><relatesTo>3</relatesTo><i> cristina.quilescano@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Cano-Pérez%2C+Julia%22">Cano-Pérez, Julia</searchLink><relatesTo>4</relatesTo><i> juliacanoperez2@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+de+Comunicación+y+Salud%22">Revista de Comunicación y Salud</searchLink>. 2024, Vol. 14, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22TEXT+mining%22">TEXT mining</searchLink><br /><searchLink fieldCode="DE" term="%22SENTIMENT+analysis%22">SENTIMENT analysis</searchLink><br /><searchLink fieldCode="DE" term="%22SOCIAL+networks%22">SOCIAL networks</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22DISINFORMATION%22">DISINFORMATION</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Background: This article aims to analyse the existing relationship in the Spanish social network Twitter between thrombi and anticovid vaccines. Methods: Machine learning techniques and massive statistical analysis were used to determine the social networks formed their relationships, sentiment and emotion analysis and main discourses by text mining during four months. For this purpose, 915,825 messages discussing thrombosis were collected along the first four months of 2021. Results: We found a very prominent increase in messages around mid-March and a sharp rise in early April, coinciding with detected cases and temporary suspensions of certain vaccines in the United States and Europe. The main of messages came from Spain, although Mexico focuses the debate on Latin America. Conclusions: It was possible to determine an increase in messages with a high emotional charge, mainly negative, as well as disinformative and conspiratorial messages, especially from groups without significant referents. The disseminators of disinformation news about vaccines are sent by small nano-influencers or fake accounts using possible astroturfing techniques. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: Fundamentos: El presente artículo tiene como objetivo analizar la relación existente en la red social Twitter en español entre trombos y vacunas anticovid. Métodos: Se utilizaron técnicas de machine learning y análisis estadístico masivo para la determinación de redes sociales formadas, sus relaciones, análisis de sentimientos y emociones y discursos principales por minería de texto durante cuatro meses. Para ello se recogieron 915.825 mensajes que hablaran de trombosis a lo largo de los cuatro primeros meses del año 2021. Resultados: Se encontró un aumento muy destacado de mensajes alrededor desde mediados de marzo e incrementándose fuertemente a principios de abril, coincidiendo con los casos detectados y suspensiones temporales de ciertas vacunas en Estados Unidos y Europa. El principal de mensajes procede de España, aunque México centra el debate en Latinoamérica. Conclusiones: Se pudieron determinar un aumento de mensajes de alta carga emocional, principalmente negativa, así como mensajes desinformativos y conspirativos especialmente desde unos grupos sin referentes significativos. Los difusores de noticias de desinformación sobre vacunas son enviados por pequeñas cuentas nano-influencers o cuentas falsas utilizando posibles técnicas astroturfing. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Revista de Comunicación y Salud is the property of Instituto Internacional de Comunicacion y Salud and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.35669/rcys.2024.14.e307 Languages: – Code: spa Text: Spanish PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: TEXT mining Type: general – SubjectFull: SENTIMENT analysis Type: general – SubjectFull: SOCIAL networks Type: general – SubjectFull: MACHINE learning Type: general – SubjectFull: DISINFORMATION Type: general Titles: – TitleFull: VACUNAS ANTICOVID Y TROMBOSIS: EL MIEDO EN LAS REDES SOCIALES. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arce-García, Sergio – PersonEntity: Name: NameFull: Cano-Garcinuño, María-Isabel – PersonEntity: Name: NameFull: Quiles-Cano, Cristina – PersonEntity: Name: NameFull: Cano-Pérez, Julia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 21745323 Numbering: – Type: volume Value: 14 Titles: – TitleFull: Revista de Comunicación y Salud Type: main |
| ResultId | 1 |
