Tinder for teens: Youth digital intimate cultures and tech facilitated 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, there has been little in-depth research that connects Snapchat's unique features and affordances with young users' practices, behaviours, and experiences on the platform. Responding to this gap, our study used mixed methods to explore British teens' diverse social, sexual, and intimate experiences on Snapchat. We discuss how Snapchat's unique features, such as disappearing images (“Snaps”), algorithmic friend recommendations (“Quick Adds”), and geolocation tracking technology ("Snap Maps”), form new conditions and environments for teens' experiences of socialising, courtship, sexting, and technology-facilitated gender-based and sexual violence. We explore how teens'desires for intimacy underpin their motivations to continue to engage in a range of risk-taking activities—despite their awareness of the dangers involved. We conclude with recommendations for better platform specific regulation and digital literacy that pays attention to teens ' rights and agency. • Littl3e research links Snapchat's unique features and affordances to young users' behaviours and experiences. • Survey, focus groups, follow-up interviews and arts-based methodologies were used to explore youth experiences on Snapchat. • Youth use Snapchat to socialise and connect with new and existing friends, to find dating and sexual partners and exchange sexually explicit images.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".