Las imágenes como recurso fundamental de la información durante la covid-19 y la fase de vacunación en medios digitales españoles.

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  • Additional Information
    • Alternate Title:
      Images as a fundamental information resource during the covid-19 pandemic and the vaccination phase in Spanish digital media.
    • Subject Terms:
      JOURNALISM; INFORMATION resources; POLITICAL communication; MEDICAL communication; COMMUNICATION models; COVID-19 pandemic; SOCIAL reality; ANTI-vaccination movement; ARTIFICIAL intelligence; DIGITAL media; TELEVISION dramas
    • Subject Terms:
      SPAIN
    • Abstract:
      El estudio tiene como objetivo caracterizar las imágenes que acompañan a las informaciones de la pandemia de la covid-19, la vacunación y los tratamientos contra el coronavirus en los medios de comunicación digitales: ABC, Deia, EITB.eus, El Correo, elDiario.es, El Mundo, La Razón, La Vanguardia, Naiz y Público (2020-2022). Se ha trabajado con una muestra de 15.654 imágenes únicas, sobre las que se ha procedido a identificar 15 clústeres con técnicas de Inteligencia Artificial, entre las que consta el algoritmo Inception V3ylas incrustaciones en espacios vectoriales o embeddings. Se opta por una estrategia no supervisada, propias de investigaciones de corte exploratorio e inductivo. Entre los resultados destaca la identificación de distintas tipologías de imágenes utilizadas por los medios de orientación médica y sanitaria, representaciones de la muerte y del drama humano causado por la pandemia, vinculadas a la dimensión más política y económica de la campaña de vacunación. Las imágenes analizadas forman parte de la 'comunicación política': un modelo de comunicación que busca y refuerza la relación entre el gobierno y los medios de comunicación, y viceversa, para generar confianza en la gestión y en el propio sistema. La diversidad de enfoques observada es un valor positivo ya que contribuye a establecer una realidad social operativa más poliédrica y rica. Los medios muestran diferentes preferencias a la hora de representar visualmente los temas, lo que se traduce en una distribución desigual de los mismos. El estudio ha identificado las diferentes narrativas revelando diferentes usos de imágenes cargadas emocionalmente que plantean un escenario complejo. [ABSTRACT FROM AUTHOR]
    • Abstract:
      The aim of the study is to describe the images that accompanied information about the Covid-19 pandemic, vaccination and treatment of the virus in the following digital news media: ABC, Deia, EITB.eus, El Correo, elDiario.es, El Mundo, La Razón, La Vanguardia, Naiz and Público (20202022). A sample of 15,654 single images was studied, of which 15 clusters were identified with Artificial Intelligence techniques, including the algorithm Inception V3. The analysis is based on the combined use of a series of innovative techniques within the field of Communication. This methodology involves a large data set of images, along with the use of AI techniques such as embeddings. We opted for an unsupervised strategy, typical of exploratory and inductive research. Among the results, the identification of different typologies of images used by the medical and health-oriented media, representations of death and of the human drama caused by the pandemic, linked to the more political and economic dimension of the vaccination campaign, stands out. The images analyzed are part of 'political communication': a communication model that seeks and reinforces the relationship between the government and the media, and vice versa, to generate confidence in the management and in the system itself. The diversity of approaches observed is a positive value as it contributes to establish a more multifaceted and richer operational social reality. The media show different preferences when it comes to visually representing the issues, which translates into an unequal distribution of the same. The study has identified the different narratives revealing different uses of emotionally charged images that pose a complex scenario. [ABSTRACT FROM AUTHOR]
    • Abstract:
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