A Total Error Framework for Digital Traces of Humans
Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2963826999