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 .
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 document errors that may occur in studies based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework, the new "Total Error Framework for Digital Traces of Human Behavior on Online Platforms" (TED-On) identifies several types of error that are specific to the use of digital traces. By providing a standard vocabulary to describe these errors, the proposed framework is intended to advance communication and research concerning the use of digital traces in scientific social research .
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The interactions and activities of hundreds of millions of people worldwide are
Peoples' activities and opinions
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every single day. When pulled together, these data offer increasingly comprehensive pictures of both individuals and groups interacting on different platforms, but they also
online, especially on social media and other web-based platforms, offer increasingly informative pictures of the public. They promise to
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broader target populations beyond those platforms , representing an enormous
populations beyond the users of the platforms on which the traces are recorded, representing real
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. Notwithstanding the many advantages
and a complement to survey-based research. But the use
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, recent studies
brings its own complexities and new error sources to the research enterprise. Recently, researchers
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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
This article synthesizes this discussion and proposes a systematic way to categorize potential errors, inspired by
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Framework, we propose
(TSE) Framework developed for survey methodology. We introduce
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and avoid
, and document
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that are based on digital traces of humans
based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework, the new "Total Error Framework for Digital Traces of Human Behavior on Online Platforms" (TED-On) identifies several types of error that are specific to the use of digital traces. By providing a standard vocabulary to describe these errors, the proposed framework is intended to advance communication and research concerning the use of digital traces in scientific social research