Directly Observing and Characterizing Adolescents' Self-Generated Social Media Posts: Protocol for Creation and...

Area of Science:

• Social Sciences
• Psychology
• Media Studies

Background:

• Adolescent social media research often overlooks self-generated content, focusing instead on platform usage frequency and self-reports.
• Limited understanding exists regarding how adolescents' own social media posts relate to their overall well-being.

Purpose of the Study:

• To introduce and validate a researcher-observed codebook for categorizing adolescents' self-generated social media content.
• To establish a reliable methodology for analyzing longitudinal social media data from adolescents.

Main Methods:

• Employed cyberethnography and a social ecological biopsychosocial model to develop a content analysis codebook.
• Trained coders on the refined codebook, ensuring interrater reliability through rigorous testing and quality control.
• Passively observed and coded self-generated content from adolescents across platforms like Instagram, TikTok, Facebook, and X.

Main Results:

• Achieved strong interrater reliability scores across all observed platforms (ranging from 0.87 to 0.96) in the first two years of data collection.
• Demonstrated the feasibility and replicability of the developed coding system for adolescent social media data.
• The methodology allowed for contextualization of content through longitudinal observation.

Conclusions:

• Cyberethnography provides a comprehensive method for collecting and analyzing adolescent self-generated social media content.
• The developed protocol enables cross-analysis with well-being data to identify potential patterns.
• Sharing this methodology facilitates its adoption by other researchers, advancing the understanding of adolescent social media use.