TY - JOUR
T1 - Computational communication science:
T2 - A methodological catalyzer for a maturing discipline
AU - Waldherr, Annie
AU - Gonzales-Bailon, Sandra
AU - Hilbert, Martin
AU - Barnett, George
AU - Blumenstock, Joshua
AU - Contractor, Noshir
AU - Diesner, Jana
AU - Frey, Seth
AU - Pan , Jennifer
AU - Smaldino, Paul
AU - Zhang, Jingwen
AU - van Atteveldt, Wouter
AU - Lamberso, PJ
AU - Peng, Tai-Quan
AU - Shen, Cuihua (Cindy)
AU - Zhu, Jonathan J.H.
PY - 2019
Y1 - 2019
N2 - 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
AB - 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
M3 - Article
VL - 13
SP - 3912
EP - 3934
JO - International Journal of Communication (IJoC)
JF - International Journal of Communication (IJoC)
SN - 1932-8036
ER -