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Political isolation in America

Published online by Cambridge University Press:  26 February 2020

Byungkyu Lee  and
Peter Bearman
Byungkyu Lee
Affiliation:
Department of Sociology, Indiana University, 770 Ballantine Hall, 1020 E. Kirkwood Ave., Bloomington, IN, USA (email: bl11@indiana.edu)
Peter Bearman*
Affiliation:
Interdisciplinary Center for Innovative Theory and Empirics, Columbia University, 701 Knox Hall, 606 W. 122nd Street, New York, NY, USA
*
*Corresponding author. Email: psb17@columbia.edu
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Abstract

This study documents historical trends of size and political diversity in Americans’ discussion networks, which are often seen as important barometers of social and political health. Contrasting findings from data drawn out of a nationally representative survey experiment of 1,055 Americans during the contentious 2016 U.S. presidential election to data arising from 11 national data sets covering nearly three decades, we find that Americans’ core networks are significantly smaller and more politically homogeneous than at any other period. Several methodological artifacts seem unlikely to account for the effect. We show that in this period, more than before, “important matters” were often framed as political matters, and that this association probably accounts for the smaller networks.

Keywords

egocentric networkpolitical polarizationcore discussion networknetwork sizepolitical homophilysurvey experiment

Information

Type
Research Article
Information
Network Science , Volume 8 , Issue 3: Special Issue Ego Networks , September 2020 , pp. 333 - 355
Copyright
© Cambridge University Press 2020

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Footnotes

Special Issue Editors: Brea L. Perry, Bernice A. Pescosolido, Mario L. Small, and Ann McCranie

†

Data collected by Time-sharing Experiments for the Social Sciences, NSF Grant 0818839, Jeremy Freese and James Druckman, Principal Investigators. Please direct correspondence to Byungkyu Lee (bl11@indiana.edu) or Peter Bearman (psb17@columbia.edu). We thank Annie Russian for her assistance in text coding. Financial support from the Interdisciplinary Center for Innovative Theory and Empirics (INCITE) at Columbia University is gratefully acknowledged. Replication materials are available at https://dataverse.harvard.edu/dataverse/bk.

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