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

Abstract

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.

References

  1. 1.

    Cialdini, R. B. & Trost, M. R. Social influence: social norms, conformity and compliance. in The Handbook of Social Psychology (eds. Gilbert, D. T., Fiske, S. T., & Lindzey, G.) 151–192 (McGraw-Hill, 1998).

  2. 2.

    Bond, R. M. et al. A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298 (2012).

  3. 3.

    Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).

  4. 4.

    Centola, D. The spread of behavior in an online social network experiment. Science 329, 1194–1197 (2010).

  5. 5.

    Borsari, B. & Carey, K. B. Descriptive and injunctive norms in college drinking: a meta-analytic integration. J. Stud. Alcohol 64, 331–341 (2003).

  6. 6.

    Botvin, G. J., Botvin, E. M., Baker, E., Dusenbury, L. & Goldberg, C. J. The false consensus effect: predicting adolescents’ tobacco use from normative expectations. Psychol. Rep. 70, 171–178 (1992).

  7. 7.

    Thompson, A. Journalists and Trump voters live in separate online bubbles. VICE News https://news.vice.com/en_us/article/d3xamx/journalists-and-trump-voters-live-in-separate-online-bubbles-mit-analysis-shows (8 December 2017).

  8. 8.

    Fields, J. M. & Schuman, H. Public beliefs about the beliefs of the public. Public Opin. Q. 40, 427–448 (1976).

  9. 9.

    Ross, L., Greene, D. & House, P. The ‘false consensus effect’: an egocentric bias in social perception and attribution processes. J. Exp. Soc. Psychol. 13, 279–301 (1977).

  10. 10.

    Mullen, B. et al. The false consensus effect: a meta-analysis of 115 hypothesis tests. J. Exp. Soc. Psychol. 21, 262–283 (1985).

  11. 11.

    Krueger, J. & Clement, R. W. The truly false consensus effect: an ineradicable and egocentric bias in social perception. J. Pers. Soc. Psychol. 67, 596–610 (1994).

  12. 12.

    Krueger, J. From social projection to social behaviour. Eur. Rev. Soc. Psychol. 18, 1–35 (2007).

  13. 13.

    Mullen, B., Dovidio, J. F., Johnson, C. & Copper, C. In-group-out-group differences in social projection. J. Exp. Soc. Psychol. 28, 422–440 (1992).

  14. 14.

    Suls, J. & Wan, C. K. In search of the false-uniqueness phenomenon: fear and estimates of social consensus. J. Pers. Soc. Psychol. 52, 211–217 (1987).

  15. 15.

    Miller, D. T. & McFarland, C. Pluralistic ignorance: when similarity is interpreted as dissimilarity. J. Pers. Soc. Psychol. 53, 298–305 (1987).

  16. 16.

    Prentice, D. & Miller, D. T. Pluralistic ignorance and alcohol use on campus: some consequences of misperceiving the social norm. J. Pers. Soc. Psychol. 64, 243–256 (1993).

  17. 17.

    Lerman, K., Yan, X. & Wu, X.-Z. The ‘majority illusion’ in social networks. PLoS One 11, e0147617 (2016).

  18. 18.

    Krueger, J. & Clement, R. W. Estimates of social consensus by majorities and minorities: the case for social projection. Pers. Soc. Psychol. Rev. 1, 299–313 (1997).

  19. 19.

    Sherman, S. J., Presson, C. C., Chassin, L., Corty, E. & Olshavsky, R. The false consensus effect in estimates of smoking prevalence: underlying mechanisms. Pers. Soc. Psychol. Bull. 9, 197–207 (1983).

  20. 20.

    Galesic, M., Olsson, H. & Rieskamp, J. A sampling model of social judgment. Psychol. Rev. 125, 363 (2018).

  21. 21.

    Juslin, P., Winman, A. & Hansson, P. The naïve intuitive statistician: a naïve sampling model of intuitive confidence intervals. Psychol. Rev. 114, 678–703 (2007).

  22. 22.

    Pachur, T., Hertwig, R. & Rieskamp, J. Intuitive judgments of social statistics: how exhaustive does sampling need to be? J. Exp. Soc. Psychol. 49, 1059–1077 (2013).

  23. 23.

    McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001).

  24. 24.

    Jadidi, M., Karimi, F., Lietz, H. & Wagner, C. Gender disparities in science? Dropout, productivity, collaborations and success of male and female computer scientists. Adv. Complex Syst. 21, 1750011 (2018).

  25. 25.

    Miller, M. K., Wang, G., Kulkarni, S. R., Poor, H. V. & Osherson, D. N. Citizen forecasts of the 2008 U.S. presidential election. Polit. Policy 40, 1019–1052 (2012).

  26. 26.

    Dawes, R. M. Statistical criteria for establishing a truly false consensus effect. J. Exp. Soc. Psychol. 25, 1–17 (1989).

  27. 27.

    Marks, G. & Miller, N. Ten years of research on the false-consensus effect: an empirical and theoretical review. Psychol. Bull. 102, 72 (1987).

  28. 28.

    Suls, J., Wan, C. K. & Sanders, G. S. False consensus and false uniqueness in estimating the prevalence of health-protective behaviors. J. Appl. Soc. Psychol. 18, 66–79 (1988).

  29. 29.

    Bianconi, G. & Barabási, A.-L. Competition and multiscaling in evolving networks. Europhys. Lett. 54, 436 (2001).

  30. 30.

    Fiedler, K. & Krueger, J. I. More than an artifact: regression as a theoretical construct. in Social Judgment and Decision Making 171–189 (Psychology Press, 2012).

  31. 31.

    Fiedler, K. & Unkelbach, C. Regressive judgment: implications of a universal property of the empirical world. Curr. Dir. Psychol. Sci. 23, 361–367 (2014).

  32. 32.

    Karimi, F., Génois, M., Wagner, C., Singer, P. & Strohmaier, M. Homophily influences ranking of minorities in social networks. Sci. Rep. 8, 11077 (2018).

  33. 33.

    Newman, M. E. Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003).

  34. 34.

    Aral, S. & Walker, D. Identifying influential and susceptible members of social networks. Science 337, 337–41 (2012).

  35. 35.

    Golub, B. & Jackson, M. O. Naive learning in social networks and the wisdom of crowds. Am. Econ. J. Microecon. 2, 112–149 (2010).

  36. 36.

    Becker, J., Brackbill, D. & Centola, D. Network dynamics of social influence in the wisdom of crowds. Proc. Natl Acad. Sci. USA 114, E5070–E5076 (2017).

  37. 37.

    DeGroot, M. H. Reaching a consensus. J. Am. Stat. Assoc. 69, 118–121 (1974).

  38. 38.

    Fiedler, K. Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychol. Rev. 107, 659 (2000).

  39. 39.

    Gigerenzer, G., Fiedler, K. & Olsson, H. Rethinking cognitive biases as environmental consequences. in Ecological Rationality: Intelligence in the World 80–110 (Oxford Univ. Press, 2012).

  40. 40.

    Le Mens, G. & Denrell, J. Rational learning and information sampling: on the ‘naivety’ assumption in sampling explanations of judgment biases. Psychol. Rev. 118, 379–392 (2011).

  41. 41.

    Denrell, J. & Le Mens, G. Information sampling, belief synchronization, and collective illusions. Manag. Sci. 63, 528–547 (2016).

  42. 42.

    Krueger, J. On the perception of social consensus. in Advances in Experimental Social Psychology 163–240 (Academic Press, 1998).

  43. 43.

    Centola, D. An experimental study of homophily in the adoption of health behavior. Science 334, 1269–1272 (2011).

  44. 44.

    Mollica, K. A., Gray, B. & Treviño, L. K. Racial homophily and its persistence in newcomers’ social networks. Organ. Sci. 14, 123–136 (2003).

  45. 45.

    Mehra, A., Kilduff, M. & Brass, D. J. At the margins: a distinctiveness approach to the social identity and social networks of underrepresented groups. Acad. Manag. J. 41, 441–452 (1998).

  46. 46.

    Festinger, L. A theory of social comparison processes. Hum. Relat. 7, 117–140 (1954).

  47. 47.

    Suls, J., Martin, R. & Wheeler, L. Social comparison: why, with whom, and with what effect? Curr. Dir. Psychol. Sci. 11, 159–163 (2002).

  48. 48.

    Mobilia, M. Does a single zealot affect an infinite group of voters? Phys. Rev. Lett. 91, 028701 (2003).

  49. 49.

    Mobilia, M., Petersen, A. & Redner, S. On the role of zealotry in the voter model. J. Stat. Mech. Theory Exp. 2007, P08029 (2007).

  50. 50.

    Centola, D., Becker, J., Brackbill, D. & Baronchelli, A. Experimental evidence for tipping points in social convention. Science 360, 1116–1119 (2018).

  51. 51.

    Rocha, L. E., Liljeros, F. & Holme, P. Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7, e1001109 (2011).

  52. 52.

    Holme, P., Edling, C. R. & Liljeros, F. Structure and time evolution of an internet dating community. Soc. Netw. 26, 155–174 (2004).

  53. 53.

    Holme, P., Liljeros, F., Edling, C. R. & Kim, B. J. Network bipartivity. Phys. Rev. E 68, 056107 (2003).

  54. 54.

    Traud, A. L., Mucha, P. J. & Porter, M. A. Social structure of facebook networks. Physica A 391, 4165–4180 (2012).

  55. 55.

    Karimi, F., Wagner, C., Lemmerich, F., Jadidi, M. & Strohmaier, M. Interring gender from names on the web: a comparative evaluation of gender detection. in Proceedings of WWW ’16 Companion 53–54 (International World Wide Web Conferences Steering Committee, 2016).

  56. 56.

    Collaboration networks from DataBase systems and Logic Programming (DBLP). http://dblp.uni-trier.de/ (accessed 30 September 2016).

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Acknowledgements

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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