The Invisible Gaze: Algorithmic Sexual Optimization of Minors on TikTok, Instagram, and YouTube

This paper investigates the unconscious structure of algorithmic sexual exploitation embedded in major social platforms such as TikTok, Instagram, and YouTube. By analyzing AI recommendation systems that learn from nonverbal signals—such as gaze duration, replays, and scroll behavior—we reveal how these systems drive young users, particularly underage girls, toward sexually optimized visual patterns. Rather than blaming users or condemning content, this work dissects the threefold relationship between platform designers, unaware viewers, and self-optimizing content creators, and exposes how engagement-driven feedback loops silently reinforce exploitative behavior. Framed from the perspective of a detached observer, this essay argues that platforms have transformed from spaces of meaning and education into desire optimization engines—while ethical responsibility remains unclaimed by all parties involved. This is not a moralistic critique, but a structural exposure of how gaze, algorithm, and profit have silently converged into one of the most dangerous feedback loops of the digital age.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-15
DOI
https://doi.org/10.5281/zenodo.17121858
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

The Invisible Gaze: Algorithmic Sexual Optimization of Minors on TikTok, Instagram, and YouTube

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

The Invisible Gaze: Algorithmic Sexual Optimization of Minors on TikTok, Instagram, and YouTube

article en

Abstract

This paper investigates the unconscious structure of algorithmic sexual exploitation embedded in major social platforms such as TikTok, Instagram, and YouTube. By analyzing AI recommendation systems that learn from nonverbal signals—such as gaze duration, replays, and scroll behavior—we reveal how these systems drive young users, particularly underage girls, toward sexually optimized visual patterns. Rather than blaming users or condemning content, this work dissects the threefold relationship between platform designers, unaware viewers, and self-optimizing content creators, and exposes how engagement-driven feedback loops silently reinforce exploitative behavior. Framed from the perspective of a detached observer, this essay argues that platforms have transformed from spaces of meaning and education into desire optimization engines—while ethical responsibility remains unclaimed by all parties involved. This is not a moralistic critique, but a structural exposure of how gaze, algorithm, and profit have silently converged into one of the most dangerous feedback loops of the digital age.

Zenodo (CERN European Organization for Nuclear Research)
Ion Physics (United States) (US)
Gender equality
Openalex Percentile: Top 43%
Geochemistry and Geologic Mapping
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