Seminars & Colloquia
"Toward a Framework for Inferring Individual-Level Characteristics from Digital Trace Data"
Wednesday March 15, 2017 05:30 PM
Location: Mountains Ballroom, Talley Student Union
(Visitor parking instructions)
This talk is part of the Data Science series
Abstract: Digital traces—records of online activity automatically recorded by the servers that undergird all online activity—allow us to explore age-old communication research questions in unprecedented ways. But one of the greatest challenges in doing so is managing the gap between the research’s conceptual focus and the set of readily available traces. Not every type of trace will be equally valuable from a particular research standpoint, and not every interesting concept will be measurable using the traces to which researchers have access. The purpose of this presentation is to contribute to the development of a framework for assessing the construct validity of conceptual inferences drawn from digital traces. In it, I will define four platform-independent domains researchers should bear in mind when choosing traces for analysis: technical design, terms of service (TOS), social context, and the potential for misrepresentation. I will illustrate the value of this framework in discussions of three individual-level characteristics of broad interest to communication researchers and others: gender, race/ethnicity, and geographic location.
Short Bio: Deen Freelon is an associate professor in the School of Communication at American University in Washington DC. He has two major areas of expertise: 1) political expression through digital media, and 2) the use of code and computational methods to extract, preprocess, and analyze very large digital datasets. Freelon has authored or co-authored over 30 journal articles, book chapters, and public reports, in addition to co-editing one scholarly book. He has served as co-principal investigator on grants from the Spencer Foundation and the US Institute of Peace thus far. He is the creator of ReCal, an online intercoder reliability application that has been used by thousands of researchers around the world; and TSM, a network analysis module for the Python programming language.
Host: Trey Overman, Laboratory for Analytic Sciences
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