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.
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