How Can Computational Social Science Motivate the Development of Theo
ries, Data, and Methods to Advance Our Understanding of Communication
and Organizational Dynamics?
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Subscriber: Northwestern University; date: 23 March 2020
Print Publication Date: Feb 2020
Subject: Political Science, Political Behavior, Political Methodology
Online Publication Date: Mar 2019 DOI: 10.1093/oxfordhb/9780190460518.013.7
How Can Computational Social Science Motivate the
Development of Theories, Data, and Methods to Ad
vance Our Understanding of Communication and Orga
nizational Dynamics?
Noshir Contractor
The Oxford Handbook of Networked Communication
Edited by Brooke Foucault Welles and Sandra González-Bailón
Abstract and Keywords
It is almost a decade since the article “Computational Social Science” was published in
Science (Lazer et al., 2009). That article advocated for computational social science as a
promising new arrow in the quiver for understanding and enabling social systems. The
chapters in this section, and indeed in this book, are a testament to the decade-long tra
jectory of this movement. The chapters in this section also provide an opportunity to re
flect on how computational social science can motivate the development of theories, data,
and methods to advance understanding of current and emerging forms of communication
and organizational dynamics. This chapter reviews some of the progress made on these
dimensions and points to chapters in the section that serve as exemplars.
Keywords: computational social science, organizational dynamics, communication theory, social network theory,
big data
1. Can Computational Social Science Motivate
the Development of Theories of Communica
tion and Organizational Dynamics?
THE advancement of organizational dynamics and communication theory is the most im
portant benchmark by which the long-term impact of a new intellectual approach is evalu
ated here. In this section I outline four ways that computational social science is motivat
ing developments that (1) test existing theories at scale; (2) extend existing theories to of
fer more nuanced insights; (3) generate new theories about existing
(p. 114)
phenomena
by the inclusion and juxtaposition of concepts for which data were either unavailable or