Interactive visualizations for adolescents to understand and challenge algorithmic profiling in online platforms

TL;DR

This paper presents Algorithmic Mirror, an interactive tool that helps adolescents explore and understand how online platforms profile them, fostering critical reflection and advocating for greater transparency and agency.

Contribution

It introduces a novel visualization tool that personalizes data insights for adolescents using their own digital footprints across multiple platforms.

Findings

Adolescents uncovered the extent of data collection and profiling.

Participants recognized cross-platform data aggregation.

Engagement fostered critical reflection on algorithmic categorizations.

Abstract

Social media platforms regularly track, aggregate, and monetize adolescents' data, yet provide them with little visibility or agency over how algorithms construct their digital identities and make inferences about them. We introduce Algorithmic Mirror, an interactive visualization tool that transforms opaque profiling practices into explorable landscapes of personal data. It uniquely leverages adolescents' real digital footprints across YouTube, TikTok, and Netflix, to provide situated, personalized insights into datafication over time. In our study with 27 participants (ages 12--16), we show how engaging with their own data enabled adolescents to uncover the scale and persistence of data collection, recognize cross-platform profiling, and critically reflect algorithmic categorizations of their interests. These findings highlight how identity is a powerful motivator for adolescents' desire for greater digital agency, underscoring the need for platforms and policymakers to move toward structural reforms that guarantee children better transparency and the agency to influence their online experiences.