Computational Communication Science| Computational Communication Science: A Methodological Catalyzer for a Maturing Discipline

Authors

  • Martin Hilbert University of California, Davis; Dpt. of Communication
  • George Barnett Distinguished Professor; University of California, Davis
  • Joshua Blumenstock Assistant Professor; University of California, Berkeley
  • Noshir Contractor Professor; Northwestern University
  • Jana Diesner Associate Professor; University of Illinois at Urbana-Champaign
  • Seth Frey Assistant Professor; University of California, Davis
  • Sandra González-Bailón Assistant Professor; University of Pennsylvania
  • PJ Lamberson Assistant Professor; University of California, Los Angeles
  • Jennifer Pan Assistant Professor; Stanford University, Dpt. of Communication
  • Tai-Quan Peng Associate Professor; Michigan State University, Dpt. of Communication
  • Cuihua (Cindy) Shen Associate Professor; University of California, Davis; Dpt. of Communication
  • Paul E. Smaldino Assistant Professor; University of California, Merced; Cognitive and Information Sciences
  • Wouter van Atteveldt Associate Professor; Vrije University Amsterdam; Dpt. of Communication Science
  • Annie Waldherr Junior Professor; University of Münster; Institut für Kommunikationswissenschaft
  • Jingwen Zhang Assistant Professor; University of California, Davis; Dpt. of Communication
  • Jonathan J. H. Zhu Professor; City University of Hong Kong; Dept. of Media and Communication

Keywords:

computational science, research methods, big data, simulations, online experiments, methodology.

Abstract

This article reviews the opportunities and challenges for computational research methods in the field of communication. Among the social sciences, communication stands out as a discipline with a relatively low-profile institutionalized focus on the in-house development of methods. Computational tools are changing this, and they are catalyzing a new set of methods directly suited to tackling foundational research questions in communication. We systematically review how computational methods affect the three fundamental pillars of the scientific method: observational approaches (i.e., digital trace data), theoretical approaches (i.e., computer simulations), and experimental research (i.e., virtual labs and field experiments). We stress that data are a catalyzer but not a requirement for computational science. We explore how observational, theoretical, and experimental approaches can be combined and cross-fertilize one another. We conclude that taking advantage of computational methods will require a systematic effort in our discipline to develop and adjust these methods.

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Published

2019-09-08

Issue

Section

Special Sections

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