dorsal/arxiv
View SchemaInteractive visualizations for adolescents to understand and challenge algorithmic profiling in online platforms
| Authors | Yui Kondo, Kevin Dunnell, Isobel Voysey, Qing Hu, Victoria Paesano, Phi H Nguyen, Qing Xiao, Jun Zhao, Luc Rocher |
|---|---|
| Categories | |
| ArXiv ID | 2601.07381vv1 |
| URL | https://arxiv.org/abs/2601.07381 |
| License | http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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.
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"abstract": "Social media platforms regularly track, aggregate, and monetize adolescents\u0027 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\u0027 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\u0027 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.",
"arxiv_id": "2601.07381",
"authors": [
"Yui Kondo",
"Kevin Dunnell",
"Isobel Voysey",
"Qing Hu",
"Victoria Paesano",
"Phi H Nguyen",
"Qing Xiao",
"Jun Zhao",
"Luc Rocher"
],
"categories": [
"cs.HC"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"title": "Interactive visualizations for adolescents to understand and challenge algorithmic profiling in online platforms",
"url": "https://arxiv.org/abs/2601.07381",
"version": "v1"
},
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"type": "Model",
"variant": "snapshot-2026-01-17",
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