Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Computational Social Science, GESIS, Cologne, Germany.
Nat Hum Behav. 2019 Oct;3(10):1078-1087. doi: 10.1038/s41562-019-0677-4. Epub 2019 Aug 12.
People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.
人们对社交网络中少数群体规模的看法可能存在偏差，通常表现为系统的高估或低估。这些社会感知偏差通常归因于有偏差的认知或动机过程。在这里，我们表明，对少数群体规模的高估和低估都可以仅从社交网络的结构属性中产生。使用生成网络模型，我们表明这些偏差取决于同质性水平、其非对称性性质以及少数群体的规模。我们的模型预测与来自跨文化调查的经验数据以及来自六个真实网络的数值计算结果非常吻合。我们还确定了个体可以通过依赖对邻居的看法来减少偏见的情况。这项工作增进了我们对网络结构对社会感知偏差的影响的理解，并为解决社会中相关问题提供了一种定量方法。
