Platform Ecosystems Matter: A Multi-Platform Risk Typology and Policy Simulation for Student Digital Wellness Safeguards: A Methodological Proof of Concept

Digital wellness policies in universities often rely on uniform daily screen time caps, yet platform-specific risks may shape student outcomes as strongly as total time online. This paper presents a methodological proof of concept: it demonstrates an analytical pipeline that converts multi-platform outcome data into a graduated, affordance-informed policy typology, rather than offering validated platform-risk classifications or ready-to-implement policy. Using a publicly available student dataset covering twelve social networks, we aggregate four harm-aligned outcomes—addictive use, poorer mental health, shorter sleep duration, and interpersonal conflict—to the platform level and apply hierarchical clustering to derive a four-tier risk typology. We stress that the clustering and the subsequent principal component analysis operate on only twelve platform-level observations, not on the 705 individual students. We then project the tiers onto a two-dimensional risk map and simulate three illustrative policy regimes: a uniform daily cap, a tier-calibrated cap, and an affordance-targeted short-video cap. In this illustrative dataset, the tier-calibrated cap reproduces roughly seventy percent of the harm reduction in a blanket cap while directly affecting about three-quarters of students, and the three regimes deliver near-equivalent harm reduction per percent of students affected. Because the source data show characteristics consistent with synthetic or semi-synthetic simulation and lack established provenance, all quantitative results are presented as demonstrations of the pipeline. We describe the sensitivity of the typology to clustering choices, the ethical risks of tiered classification, and the replication required before any tier could inform practice.