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感知到的群体凝聚力与实际社会结构:一项使用自我中心Facebook网络的社会网络分析的研究。

Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks.

作者信息

Tulin Marina, Pollet Thomas V, Lehmann-Willenbrock Nale

机构信息

University of Amsterdam, The Netherlands.

Northumbria University, United Kingdom.

出版信息

Soc Sci Res. 2018 Aug;74:161-175. doi: 10.1016/j.ssresearch.2018.04.004. Epub 2018 Apr 24.

Abstract

Research on group cohesion often relies on individual perceptions, which may not reflect the actual social structure of groups. This study draws on social network theory to examine the relationship between observable structural group characteristics and individual perceptions of group cohesion. Leveraging Facebook data, we extracted and partitioned the social networks of 109 participants into groups using a modularity algorithm. We then surveyed perceptions of cohesion, and computed group density and size using social network analysis. Out of six linear mixed effects models specified, a random intercept and fixed slope model with group size as a predictor of perceived group cohesion emerged as best fitting. Whereas group density was not linked to perceived cohesion, size had a small negative effect on perceived cohesion, suggesting that people perceive smaller groups as more cohesive. We discuss the potential of social network analysis, visualization tools, and Facebook data for advancing research on groups.

摘要

群体凝聚力的研究通常依赖于个体认知,而个体认知可能无法反映群体的实际社会结构。本研究借鉴社会网络理论,以考察群体可观察的结构特征与个体对群体凝聚力的认知之间的关系。利用脸书数据,我们使用模块化算法提取了109名参与者的社交网络并将其划分为不同群体。然后,我们调查了凝聚力认知,并运用社会网络分析计算了群体密度和规模。在指定的六个线性混合效应模型中,一个以群体规模作为感知群体凝聚力预测指标的随机截距和固定斜率模型拟合效果最佳。虽然群体密度与感知凝聚力没有关联,但规模对感知凝聚力有微小的负面影响,这表明人们认为较小的群体凝聚力更强。我们讨论了社会网络分析、可视化工具以及脸书数据在推进群体研究方面的潜力。

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