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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0, Open
Conference proceeding
Unfiltered: How Teens Engage in Body Image and Shaming Discussions via Instagram Direct Messages (DMs)
Proceedings of the ACM on Human-Computer Interaction, v 9(7), pp 1-29
16 Oct 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
We analyzed 1,596 sub-conversations within 451 direct message (DM) conversations from 67 teens (ages 13-17) who engaged in private discussions about body image on Instagram. Our findings show that teens often receive support when sharing struggles with negative body image, participate in criticism when engaging in body-shaming, and are met with appreciation when promoting positive body image. Additionally, these types of disclosures and responses varied based on whether the conversations were one-on-one or group-based. We found that sharing struggles and receiving support most often occurred in one-on-one conversations, while body shaming and negative interactions often occurred in group settings. A key insight of the study is that private social media settings can significantly influence how teens discuss and respond to body image issues. Based on these findings, we propose design guidelines for social media platforms that can foster positive interactions around body image, ultimately creating a healthier and more supportive online environment for teens struggling with body image concerns.
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Details
- Title
- Unfiltered: How Teens Engage in Body Image and Shaming Discussions via Instagram Direct Messages (DMs)
- Creators
- Abdulmalik Alluhidan - Vanderbilt UniversityJinkyung Katie Park - Clemson UniversityMamtaj Akter - New York Institute of TechnologyRachel Rodgers - Northeastern UniversityAfsaneh Razi (Corresponding Author) - Drexel University, Information SciencePamela J. Wisniewski
- Publication Details
- Proceedings of the ACM on Human-Computer Interaction, v 9(7), pp 1-29
- Publisher
- Association for Computing Machinery
- Number of pages
- 29
- Grant note
- U.S. National Science Foundation: IIP-2329976, IIS-2333207 William T. Grant Foundation: 187941
Acknowledgments This research is supported in part by the U.S. National Science Foundation under grants #IIP-2329976, #IIS-2333207, and by the William T. Grant Foundation grant #187941. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the research sponsors. We would also like to thank Nafees-ul Haque and Sarvech Qadir for their support with relevancy coding and data retrieval. We also extend our gratitude to all the participants who donated their data and contributed towards our research.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001615029900005
- Scopus ID
- 2-s2.0-105019581245
- Other Identifier
- 991022122863804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Information Systems