Hilbert, M., Cingel, D. P., Zhang, J., Vigil, S. L., Shawcroft, J., Xue, H., Thakur, A., & Shafiq, Z. (2025). #BigTech @Minors: Social media algorithms have actionable knowledge about child users and at-risk teens. Telematics and Informatics, 103, 102341. https://doi.org/10.1016/j.tele.2025.102341
https://authors.elsevier.com/a/1m8zE2dUkYfBb6
This study explores whether social media platforms’ proprietary recommendation algorithms could possess actionable knowledge about the age and potential vulnerabilities of minors who misrepresent their age during account creation. Our results show that the algorithms of YouTube, Instagram, and TikTok quickly and confidently adjust (unsolicited) recommendations for accounts with behavioral traits from underage users after just one online session. Users behaving like 8-year-olds receive almost seven times more child-directed content than their 16-year-old peers. The tailoring of commercial offerings demonstrates that providers possess sufficient knowledge to prompt action. By law, children under the age of 13 are prohibited from being in a commercial space that collects personal data without parental consent. Algorithms also reacted to accounts behaving like struggling adolescents, which received over 30% more problematic- and over 70% more distressing content than their non-struggling peers. The visible adjustment of content offerings, based on this demonstrable detection ability, raises the question of whether and how this communicated information from the algorithms could be used to enhance protections of minors. This algorithm audit is part of a wider research agenda showcasing the feasibility and value of independent audits to address (often unintended) loopholes stemming from automated algorithmic decision-making processes.
More on the ongoing discussions and the political debate here:
https://www.ucdavis.edu/magazine/risks-ai-mirror-social-media
