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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Sep 19, 2025
Date Accepted: Feb 17, 2026

The final, peer-reviewed published version of this preprint can be found here:

Directly Observing and Characterizing Adolescents' Self-Generated Social Media Posts: Protocol for Creation and Implementation of a Cyberethnography Informed Codebook

Boyd K, Bliss L, Fan T, Kern K, Calhoun C, Moreno MA, Cascio CN, Selkie E

Directly Observing and Characterizing Adolescents' Self-Generated Social Media Posts: Protocol for Creation and Implementation of a Cyberethnography Informed Codebook

JMIR Res Protoc 2026;15:e84461

DOI: 10.2196/84461

PMID: 41915455

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Directly Observing and Characterizing Adolescents' Self-generated Social Media Posts: Creation and Implementation of a Cyberethnography Informed Codebook

  • Kylie Boyd; 
  • Lydia Bliss; 
  • Tingting Fan; 
  • Kayla Kern; 
  • Caitlin Calhoun; 
  • Megan A Moreno; 
  • Christopher N Cascio; 
  • Ellen Selkie

ABSTRACT

Introduction: Adolescent social media research has primarily focused on frequency of platform use and self-report measures. There has been limited focus on the self-generated content posted by adolescents and how this might relate to their wellbeing. This manuscript describes a researcher-observed codebook for characterizing adolescents’ self-generated content in a longitudinal sample.

Methods:

Participants in the study provided informed assent (and parental informed consent) for researchers to follow them and passively observe their self-generated content on Instagram, TikTok, Facebook, and X (formerly known as Twitter). Guided by Bronfenbrenner’s social ecological biopsychosocial model, the research team created a codebook incorporating prior cyberethnographic observation of self-generated social media content. After codebook refinement, coders (research staff and student research assistants) were trained through multiple rounds of test coding, and the codebook was applied to participant data with periodic quality control measures to ensure interrater reliability.

Results:

The study sample includes 344 participants (mean age = 13.89 years) with a total of 4,886 participant-months coded by 28 coders between April 2023 and June 2025. Interrater reliability agreement scores (AC1) have shown strong interrater reliability; For Year 1, scores were Facebook 0.89, Instagram 0.89, TikTok 0.88, X 0.87, and combined 0.88; for Year 2: Facebook 0.95, Instagram 0.96, TikTok 0.96, X 0.96, and combined 0.96. Discussion: This project provides replicable guidance to categorize social media data from adolescent participants using human coders who can contextualize content through longitudinal observation. The method that our team chose and followed paved the way for many strengths to be recognized as well as lessons learned by our team that allowed for adaptation and growth to occur. This granted the team the opportunity to recognize the importance of the chosen methodology, which ultimately led to the conclusion of showing the relationship social media and socio-emotional wellbeing along with the need for further research to be explored on this topic.


 Citation

Please cite as:

Boyd K, Bliss L, Fan T, Kern K, Calhoun C, Moreno MA, Cascio CN, Selkie E

Directly Observing and Characterizing Adolescents' Self-Generated Social Media Posts: Protocol for Creation and Implementation of a Cyberethnography Informed Codebook

JMIR Res Protoc 2026;15:e84461

DOI: 10.2196/84461

PMID: 41915455

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