Cast framework measures social media use across multiple levels
The CAST-framework: Measure and model social media use as a multi-level phenomenon through real-world applications
Human-Computer InteractionComputers and SocietySocial and Information Networks
Summary
Measuring how people use social media just by total screen time misses important details about what they see, do, and feel. The authors created the CAST framework to track social media use by combining data from phones, wearables, surveys, and user reactions in real time. This approach captures behaviors and experiences at different times and places, helping understand how social media affects well-being more precisely. They showed that simple daily summaries can hide opposite effects of different activities, highlighting why detailed, synchronized measurements matter.
What this means in practice
- •For mobile app developers: Design and evaluate social media features using multi-level user data for personalized well-being support.
- •For health technology teams: Integrate synchronized phone, wearable, and self-report data to build better digital well-being interventions.
Tested on simulated data.
Authors
David Grüning, Jasper Doeninghaus, Zina Efchary, Yui Kondo, Kevin Dunnell, Lennart Fischer, Isabella Zimmermann, Linnea Körte, Leo Mehlig, Frederik Riedel, Paul Schmiedmayer
Abstract
Designing social media experiences that support well-being requires understanding when, how, and for whom use matters. Screen-time totals omit content and context, and connecting these with behavior and experience requires coordinating measurements across timescales. We introduce the CAST framework to connect measurement choices with person-specific models of exposure, behavior, physiology, and experience. Its dimensions specify where observations occur, how they are obtained, what they measure, and at what temporal resolution. Responses to interventions, such as whether to proceed after an app-opening pause, enter as behavioral measurements. We propose four synchronized measurement modules linking mobile and wearable data with self-reports and intervention responses. A synthetic demonstration with 120 simulated participants over 28 days illustrates how daily aggregation can obscure opposing effects of different activities under specified generating assumptions. The framework guides selection of measures and outcomes for evaluating social media interfaces and interventions.