Political isolation in America

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY Network Science Pub Date : 2020-09-01 DOI:10.1017/nws.2020.9
Byungkyu Lee, P. Bearman
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引用次数: 9

Abstract

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.
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美国的政治孤立
摘要本研究记录了美国人讨论网络规模和政治多样性的历史趋势,这些网络通常被视为社会和政治健康的重要晴雨表。将2016年有争议的美国总统选举期间对1055名美国人进行的一项具有全国代表性的调查实验的数据与涵盖近三十年的11个国家数据集的数据进行对比,我们发现美国人的核心网络比其他任何时期都要小得多,政治上也更为同质。一些方法上的人工制品似乎不太可能解释这种影响。我们表明,在这一时期,“重要事项”比以前更多地被定义为政治事项,这种关联可能是较小网络的原因。
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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