Homophily and minority-group size explain perception biases in social networks

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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.

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Fig. 1: Individual- and group-level social perception bias.
Fig. 2: Survey results: bias in perception of minority-group size for participants whose personal networks exhibit different levels of homophily (h) and for attributes held by a small, medium or large minority group in a given country.
Fig. 3: Network model results: bias in perception of minority-group size for the minority and majority groups, as a function of homophily (h) and the minority fraction (fm) in the overall network.
Fig. 4: Numerical simulations: group-level social perception biases that could occur in six empirical social networks.
Fig. 5: Social perception biases for individual nodes and for the weighted average of perceptions of individual nodes and their neighbours.

Data availability

The three empirical data (DBLP, GitHub, APS) can be found online at https://github.com/frbkrm/NtwPerceptionBias. The network data for Brazil can be found in the data description of the study51 published in PLoS Computational Biology in 2011. POK can be found from the corresponding authors of the study52 published in Social Networks in 2004, and USF51 can be found from the corresponding author of the study54 in Physica A, 2011. The survey data can be obtained from the authors upon request.

Code availability

The Python scripts used for the generative model and empirical network analyses are available online at https://github.com/frbkrm/NtwPerceptionBias. Additional information about codes is available from the corresponding authors upon request.


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We thank K. Winters, J. Kohne, P. Holme and H. Olsson for insightful conversations. We thank the Complex Systems Society’s Bridge Fund and GESIS for funding E.L.’s research visit. E.L. was financially supported by a MURI grant to E. B. Falk from the Army Research Office (No. W911NF-18-1-0244), with additional financial support from grants to P. J. Mucha from the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health (No. R01HD075712) and the James S. McDonnell Foundation (grant No. 220020315). H.-H.J. acknowledges financial support from the Basic Science Research Program through an NRF grant funded by the Ministry of Education (No. NRF-2018R1D1A1A09081919). M.G. acknowledges financial support from National Science Foundation grants Nos. 1745154 and 1757211, and United States Department of Agriculture, National Institute of Food and Agriculture grant No. 2018-67023-27677. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

E.L. and F.K. conducted the analyses of synthetic and empirical networks and wrote the code. E.L. and M.G. conducted and analysed the surveys. E.L., F.K., C.W., H.-H.J., M.S. and M.G. conceived the project, developed the argument and wrote the paper.

Correspondence to Eun Lee or Fariba Karimi.

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Supplementary Tables 1–3, Supplementary Figs. 1–6, Supplementary Result 1, Supplementary Method 1 and Supplementary References.

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