自我是有系统偏见的社会窗口

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Network Science Pub Date : 2020-09-01 DOI:10.1017/nws.2020.5
S. Feld, Alec McGail
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引用次数: 5

摘要

摘要一个人的自我网,即与该人有联系的其他人的集合,是社会的个人样本,它特别影响该人对社会的体验和看法。我们发现,自我网络系统性地歪曲了普通人群,因为每个人都包含在与他有“朋友”一样多的自我网络中。先前的研究已经认识到,自我网络中的这种不平等权重导致许多人发现,他们的朋友比他们自己有更多的朋友。本文以这项研究为基础,表明人们的自我网络为他们提供了更普遍的有系统偏见的人群样本。我们讨论了这种普遍存在的自我网络偏见如何对人们的经历和对他人关系和特征频率的感知产生深远影响,从而影响他们自己的感受和行为。特别是,这些自我网络偏见可能有助于解释人们过度体验和高估某些类型的越轨行为和其他社会行为的普遍性,从而受到影响的倾向。我们通过对63731名脸书用户中所有朋友的分析来说明自我偏见。我们呼吁对自我网络偏见及其对个人和社会的影响进行进一步的实证调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Egonets as systematically biased windows on society
Abstract A person’s egonet, the set of others with whom that person is connected, is a personal sample of society which especially influences that person’s experience and perceptions of society. We show that egonets systematically misrepresent the general population because each person is included in as many egonets as that person has “friends.” Previous research has recognized that this unequal weighting in egonets leads many people to find that their friends have more friends than they themselves have. This paper builds upon that research to show that people’s egonets provide them with systematically biased samples of the population more generally. We discuss how this ubiquitous egonet bias may have far reaching implications for people’s experiences and perceptions of frequencies of other people’s ties and traits in ways that may influence their own feelings and behaviors. In particular, these egonet biases may help explain people’s tendencies to disproportionately experience and overestimate the prevalence of certain types of deviance and other social behaviors and consequently be influenced toward them. We illustrate egonet bias with analyses of all friends among 63,731 Facebook users. We call for further empirical investigation of egonet biases and their consequences for individuals and society.
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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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