TINDER FOR TEENS: AN IN-DEPTH EXPLORATION OF YOUTH INTIMATE CULTURES AND SEXUAL AND GENDER-BASED VIOLENCE ON SNAPCHAT
Bibliographic record
Abstract
Snapchat has long been a pivotal space for youth digital intimate and sexual cultures, as well as gendered and sexual risks and harms. Despite being one of the most widely used social media platforms among youth in England and America, there has been little in-depth research that connects Snapchat’s unique features and affordances with an analysis of young users’ practices, behaviours, and experiences on the platform. Responding to this gap, our paper explores our mixed-methods research findings on British young people’s diverse social, sexual, and intimate experiences on Snapchat. We explore how Snapchat’s unique features, such as disappearing images (“Snaps”), algorithmic friend recommendations (“Quick Adds”), and user engagement metric ("Snapscores”), form new conditions and environments for young people’s experiences of digital courtship, sexting, and sexual and gender-based violence. In addition, we contextualise youth user experiences with Snapchat’s community guidelines, safeguards, and protections for youth, which we argue fail to understand or address the actual lived experiences of youth users. We conclude with recommendations for interventions dedicated to increasing platform-specific digital literacy (particularly for parents, policymakers, and educators), and preventing and responding to youth experiences of online gendered risks and harm—while upholding their digital and sexual rights.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".