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An Introduction to Computational Social Science for Organizational Communication

An Introduction to Computational Social Science for Organizational Communication
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Author(s): Andrew N. Pilny (University of Kentucky, USA) and Marshall Scott Poole (University of Illinois at Urbana-Champaign, USA)
Copyright: 2018
Pages: 17
Source title: Transformative Practice and Research in Organizational Communication
Source Author(s)/Editor(s): Philip J. Salem (Texas State University, USA) and Erik Timmerman (University of Wisconsin, USA)
DOI: 10.4018/978-1-5225-2823-4.ch011

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Abstract

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.

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