The interactions and activities of hundreds of millions of people worldwide are recorded as digital traces every single day. When pulled together, these data offer increasingly comprehensive pictures of both individuals and groups interacting on different platforms, but they also allow inferences about broader target populations beyond those platforms, representing an enormous potential for the Social Sciences. Notwithstanding the many advantages of digital traces, recent studies have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena. Incidentally, many similar errors also affect survey estimates, which survey designers have been addressing using error conceptualization frameworks such as the Total Survey Error Framework. In this work, and leveraging the systematic approach of the Total Survey Error Framework, we propose a conceptual framework to diagnose, understand and avoid errors that may occur in studies that are based on digital traces of humans.
翻译:全世界数亿人的相互作用和活动每天都记录为数字痕迹。当汇集在一起时,这些数据提供了日益全面的个人和群体在不同平台上互动的图片,但也使人们可以推断出这些平台之外更广泛的目标人群,对社会科学来说具有巨大的潜力。尽管数字痕迹有许多好处,但最近的研究已开始讨论数字痕迹用于了解人类和社会现象时可能发生的错误。顺便提一下,许多类似的错误也影响到调查估计,调查设计者一直在利用总调查错误框架等错误概念化框架来处理这些估计。在这项工作中,我们利用总调查错误框架的系统方法,提出了一个概念框架,用以诊断、理解和避免基于人类数字痕迹的研究中可能出现的错误。