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Conference proceeding
For Me or Not for Me? The Ease With Which Teens Navigate Accurate and Inaccurate Personalized Social Media Content
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp 1-7
11 May 2024
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Abstract
Social media apps present personalized content to users. Such content is often described as “for you,” raising questions about the relationship between users’ sense of “self” and the “you” that is represented. Answering such questions is pressing in the case of teen users whose identities are still forming. Thus we ask, “What do teens think about the relationship between personalized content and their sense of self?” We interviewed teens aged 13 to 17 (n = 15) about their experiences with personalized content on social media. Participants so routinely saw themselves accurately reflected in personalized content that they noted the occasional inaccuracy with surprise, while simply scrolling past it. Our findings point to: the normalization of data doubles in the form of personalized content; and teens’ indifference to inaccuracies presented by such data doubles.
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Details
- Title
- For Me or Not for Me? The Ease With Which Teens Navigate Accurate and Inaccurate Personalized Social Media Content
- Creators
- John S Seberger (Corresponding Author) - Drexel University, Information ScienceAfsaneh Razi - Drexel University, Information ScienceNora Kathryn McDonald - George Mason University
- Publication Details
- CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp 1-7
- Conference
- CHI '24: CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA, 11 May 2024–16 May 2024)
- Publisher
- Association for Computing Machinery
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001255317907020
- Scopus ID
- 2-s2.0-85194848304
- Other Identifier
- 991021874715404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods