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Algorithmic Content Recommendations on a Video-Sharing Platform Used by Children.
Radesky, Jenny; Bridgewater, Enrica; Black, Shira; O'Neil, August; Sun, Yilin; Schaller, Alexandria; Weeks, Heidi M; Campbell, Scott W.
Afiliação
  • Radesky J; Department of Pediatrics, University of Michigan Medical School, Ann Arbor.
  • Bridgewater E; Department of Communication & Media, University of Michigan, Ann Arbor.
  • Black S; Department of Psychology, University of Michigan, Ann Arbor.
  • O'Neil A; Department of Communication & Media, University of Michigan, Ann Arbor.
  • Sun Y; Department of Communication & Media, University of Michigan, Ann Arbor.
  • Schaller A; Department of Communication & Media, University of Michigan, Ann Arbor.
  • Weeks HM; Department of Pediatrics, University of Michigan Medical School, Ann Arbor.
  • Campbell SW; Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor.
JAMA Netw Open ; 7(5): e2413855, 2024 May 01.
Article em En | MEDLINE | ID: mdl-38809550
ABSTRACT
Importance Free video-sharing platforms (VSPs) make up a high proportion of children's daily screen use. Many VSPs make algorithmic recommendations, appearing as thumbnail images from the video, which content creators use to advertise their video content.

Objective:

To explore how VSP thumbnails use attention-capture designs to encourage engagement with content and to test whether VSP algorithmic recommendations offer more problematic thumbnail features over time. Design, Setting, and

Participants:

In this cross-sectional study conducted in January 2022, researchers mimicked the search behavior of children on a popular VSP by randomly clicking on recommended videos in order to test whether thumbnail designs changed over 20 sequential video engagements. A digital, footprint-free data collection setting was created by using a new computer and wireless internet router. Data were collected from YouTube via an internet browser not logged into a user account. Data analysis occurred from April to December 2022. Exposures Manual searches using 12 top-searched terms popular with school-aged children were conducted. Researchers captured the video thumbnails recommended at the end of each video and randomly clicked subsequent videos for 20 sequential engagements. Main Outcomes and

Measures:

Thumbnail content codes were developed through iterative review of screenshots by a multidisciplinary research team and applied by trained coders (reliability, κ >.70). The prevalence of problematic thumbnail content and change in prevalence over 20 engagements was calculated using the Cochran-Armitage trend test.

Results:

A total of 2880 video thumbnails were analyzed and 6 features were coded, including visual loudness; drama and intrigue; lavish excess and wish fulfillment; creepy, bizarre, and disturbing; violence, peril, and pranks; and gender stereotypes. A high proportion contained problematic features including the creepy, bizarre, and disturbing feature (1283 thumbnails [44.6%]), violence, peril, and pranks feature (1170 thumbnails [40.6%]), and gender stereotypes feature (525 thumbnails [18.2%]). Other features included attention-capture designs such as the visual loudness feature (2278 thumbnails [79.1%]), drama and intrigue feature (2636 thumbnails [91.5%]) and lavish excess and wish fulfillment feature (1286 thumbnails [44.7%]). Contrary to the hypotheses, problematic feature prevalence did not increase over time, but the gender stereotypes feature increased with more engagement in the recommendations feed (P for trend < .001). Conclusions and Relevance In this study of video recommendations for search terms popular with children, thumbnails contained problematic and attention-capturing designs including violent, stereotyped, and frightening themes. Research is needed to understand how children respond to thumbnail designs and whether such designs influence the quality of content children consume.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Gravação em Vídeo / Algoritmos Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Gravação em Vídeo / Algoritmos Idioma: En Ano de publicação: 2024 Tipo de documento: Article