TED-On: A Total Error Framework for Digital Traces of Human Behavior on Online Platforms
Indira Sen, Fabian Floeck, Katrin Weller, Bernd Weiss, Claudia Wagner

TL;DR
This paper introduces TED-On, a comprehensive framework inspired by survey error models, to systematically categorize and understand errors in research using digital traces of human behavior on online platforms.
Contribution
It proposes a novel total error framework tailored for digital traces, enhancing error diagnosis, understanding, and communication in social science research.
Findings
Identifies specific error types unique to digital trace data
Provides a standardized vocabulary for error description
Facilitates improved research accuracy and transparency
Abstract
Peoples' activities and opinions recorded as digital traces online, especially on social media and other web-based platforms, offer increasingly informative pictures of the public. They promise to allow inferences about populations beyond the users of the platforms on which the traces are recorded, representing real potential for the Social Sciences and a complement to survey-based research. But the use of digital traces brings its own complexities and new error sources to the research enterprise. Recently, researchers have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena. This article synthesizes this discussion and proposes a systematic way to categorize potential errors, inspired by the Total Survey Error (TSE) Framework developed for survey methodology. We introduce a conceptual framework to diagnose, understand, and…
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Taxonomy
TopicsSocial Media and Politics · Privacy, Security, and Data Protection · Survey Methodology and Nonresponse
