AI 中文总结
CAST框架通过多层级测量连接社交媒体使用与个体模型，指导界面和干预评估，合成演示揭示日聚合可能掩盖活动效应。
AI 中文摘要
设计支持福祉的社交媒体体验需要理解使用的时间、方式及对谁重要。屏幕时间总量忽略了内容和情境，而将这些与行为和体验联系起来需要在不同时间尺度上协调测量。我们引入CAST框架，将测量选择与针对个体的暴露、行为、生理和体验模型相连接。其维度指定了观察发生的地点、获取方式、测量内容以及时间分辨率。对干预的响应（例如在应用打开暂停后是否继续）作为行为测量进入模型。我们提出了四个同步测量模块，将移动和可穿戴数据与自我报告及干预响应相结合。一项包含120名模拟参与者、为期28天的合成演示表明，在特定生成假设下，日聚合可能掩盖不同活动的相反效应。该框架指导选择用于评估社交媒体界面和干预措施的测量指标与结果变量。
英文摘要
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.
Comments21 pages, 3 figures, 4 tables
