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"Spilling the tea" on generation Z social media use and body image.

  • Published In: Social Development, 2023, v. 32, n. 4. P. 1409 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kiefner‐Burmeister, Allison; Domoff, Sarah; Waltz, Hayley; Jacobs, Alli; Ramirez, Clarissa; Heilman, Claire C. 3 of 3

Abstract

Over ninety percent of American teens and the majority of children have smartphones. As access to social media increases so does the growing concern for the psychological well‐being of today's youth. The current cross‐sectional study examined the media use, appearance pressure, and body image of 150 Midwestern American Generation Z (born in 1997–2012) youth. This study assessed media use by child‐report, parent‐report, and by gathering data directly from the child's smartphone. Results demonstrated that media use was higher in teens than children, but did not suggest strong gender differences. The data also showed the inconsistencies present in media use reporting, with parents overestimating the amount of time their children spend on phones. Media use was found to be unrelated to body image. Media pressure and social media integration into participants' social routines were intercorrelated and higher in the older age groups than the younger ones. Media pressure and media use was found to be less gendered than expected, but greatly shifted with age. Time spent on social media may be less influential on body image than the content of the media being consumed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Development. 2023/11, Vol. 32, Issue 4, p1409
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2023
  • ISSN:0961-205X
  • DOI:10.1111/sode.12699
  • Accession Number:173014188
  • Copyright Statement:Copyright of Social Development is the property of Wiley-Blackwell 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.)

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