参与者介导的敏感数字痕迹数据收集：CANDOR研究基础设施

中文摘要 AI 辅助

数字痕迹数据提供了日常环境中行为的丰富测量，但支持其收集的研究生态系统受到平台API访问减少和历史依赖公开可观察数据的限制。参与者介导的数据捐赠提供了一种补充方法，即个体将其数字历史中的选定部分贡献给研究。此类数据可包括纵向和非公开行为，跨越多个平台和模态，并可链接到独立收集的研究测量，从而能够实现仅使用公开社交媒体数据难以实施的研究设计。这些机会也引入了围绕参与者控制、数据最小化、异构平台导出、隐私和治理的方法论挑战，特别是在需要语义或多模态内容来研究目标构念时。我们提出了CANDOR（用于开放研究的网络数据收集与分析），这是一个用于参与者介导的敏感数字痕迹数据收集和治理的端到端基础设施。CANDOR支持参与者定向选择平台、数据类型和时间范围；模块化的平台和模态特定解析与去标识化；链接到独立研究测量；以及受保护的处理、存储和访问。我们为此类研究推导出设计需求，并将CANDOR与现有数据捐赠基础设施进行比较，识别不同方法如何支持参与者控制、数据最小化、科学必要的数据丰富性、研究设计灵活性和治理。总之，这项工作为在行为研究中使用参与者贡献的数字痕迹提供了方法论和基础设施框架，特别是当解决科学问题所需的数据是纵向、非公开、多模态或敏感时。

英文摘要

Digital trace data provide rich measures of behavior in everyday settings, but the research ecosystem supporting their collection is constrained by declining platform API access and a historical reliance on publicly observable data. Participant-mediated data donation offers a complementary approach in which individuals contribute selected portions of their own digital histories to research. Such data can include longitudinal and non-public behavior, span multiple platforms and modalities, and be linked to independently collected study measures, enabling study designs that are difficult to implement using public social media data alone. These opportunities also introduce methodological challenges around participant control, data minimization, heterogeneous platform exports, privacy, and governance, particularly when semantic or multimodal content is necessary to study the construct of interest. We present CANDOR (Collecting and Analyzing Networked Data for Open Research), an end-to-end infrastructure for participant-mediated collection and governance of sensitive digital trace data. CANDOR supports participant-directed selection of platforms, data types, and temporal ranges; modular platform- and modality-specific parsing and de-identification; linkage to independent study measures; and protected processing, storage, and access. We derive design requirements for this class of research and compare CANDOR with existing data donation infrastructures, identifying how different approaches support participant control, data minimization, scientifically necessary data richness, study-design flexibility, and governance. Together, this work provides a methodological and infrastructural framework for using participant-contributed digital traces in behavioral research, particularly when the data needed to address a scientific question are longitudinal, non-public, multimodal, or sensitive.

发表机构

• Institute for People and Technology, Georgia Institute of Technology（佐治亚理工学院人与技术研究所）
• School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）
• College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）

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