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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.
Individual- and group-level social perception bias a,b, Individuals belong to one of two groups: the majority (blue) or the minority (orange). The minority fraction is 1/3 in both (fm ≈ 0.33) the (a) homophilic network and (b) heterophilic network. We studied social perception biases originating at both the individual and group level. On the individual level, individual i perceives the size of the minority group in the overall network based on their personal network, denoted by dashed circles. In the homophilic network, i perceives the size of the minority to be approximately 1/6 ≈ 16%, while in the heterophilic network, i perceives the size of the minority to be approximately 4/6 ≈ 67%. Therefore, in the homophilic network, individual i underestimates the minority-group size by a factor of 0.5 and in the heterophilic network i overestimates the minority-group size by a factor of 2 (see equation (1)). At the group level, the majority group perceives the size of the minority group to be (1/3 + 1/6 + 2/3)/8 = 7/48 ≈ 0.15 in the homophilic network, and (1/2 + 1/3 + 2/3 + 2/3 + 1 + 1)/8 = 25/48 ≈ 0.52 in the heterophilic network. Thus, the majority group underestimates the size of the minority group by a factor of 0.45 in the homophilic network (group-level perception bias = 0.15fm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{0.15}}{{f_m}}$$\end{document}=0.150.33\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{0.15}}{{0.33}}$$\end{document} = 0.45) and overestimates the minority-group size by a factor of 1.6 in the heterophilic network (0.520.33\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{0.52}}{{0.33}}$$\end{document} = 1.6; see equation (2)). In sum, depending on the structure of the network, individuals’ and groups’ perceptions about their own and other groups’ sizes can be distorted.
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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 a–f, Each row shows results from a different country: Germany (a,b, n = 99), South Korea (c,d, n = 100) and the United States (e,f, n = 101). Columns show perception biases of the minority (left) and the majority (right) group for each attribute. Different colours distinguish perception biases for attributes that in a given country are held by a small (fm < 0.2; yellow), medium (0.2 ≤ fm < 0.4; purple) or large (0.4 ≤ fm < 0.5; red) minority group. The value of the individual perception bias indicates the accuracy with which each participant (each point in the plot) perceived the size of the minority group in the overall population. Group-level perception biases are calculated by averaging individual participants’ perception biases for each homophily bin (0.02 increments), and they are denoted by fitted lines. A perception bias of 1 suggests perfect accuracy (horizontal line in each panel), values >1 indicate overestimation of the minority-group size and values <1 indicate underestimation of the minority-group size. The insets show fitted trends on a log scale for easier comparison of the sizes of underestimation and overestimation. These trends also approximate the results for the simple difference measure of perception biases. Homophily (h) is estimated from participants’ reports about the frequency of people with each attribute in their personal networks (see Methods).
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Articles
https://doi.org/10.1038/s41562-019-0677-4
1Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 2Department of Computational Social Science, GESIS,
Cologne, Germany. 3Institute for Web Science and Technologies, University of Koblenz-Landau, Koblenz, Germany. 4Asia Pacific Center for Theoretical
Physics, Pohang, Republic of Korea. 5Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea. 6Department of
Computer Science, Aalto University, Espoo, Finland. 7Department for Society, Technology and Human Factors & Department of Computer Science, RWTH
Aachen University, Aachen, Germany. 8Santa Fe Institute, Santa Fe, NM, USA. 9Complexity Science Hub Vienna, Vienna, Austria. 10Harding Center for Risk
Literacy, Max Planck Institute for Human Development, Berlin, Germany. 11These authors contributed equally: Eun Lee, Fariba Karimi.
*e-mail: eunfeel@email.unc.edu; fariba.karimi@gesis.org
People’s perceptions of their social worlds determine their own
personal aspirations1 and willingness to engage in different
behaviours, from voting2 and energy conservation3 to health
behaviour4, drinking5 and smoking6. Yet, when forming these per-
ceptions, people seldom have an opportunity to draw representa-
tive samples from the overall social network, or from the general
population. Instead, their samples are constrained by the local
structure of their personal networks, which can bias their percep-
tion of the relative frequency of different attributes in the general
population. For example, supporters of different candidates in the
2016 US presidential election formed relatively isolated Twitter
communities7. Such insular communities can overestimate the rela-
tive frequency of their own attributes in the overall society. This
has been documented in the literature on overestimation effects
including false consensus, looking-glass perception and, more gen-
erally, social projection812. In an apparent contradiction, it has also
been documented that people holding a particular view sometimes
underestimate the frequency of that view, as described in the litera-
ture on false uniqueness13,14, pluralistic ignorance15,16 and majority
illusion17. These over- and underestimation errors, which we call
social perception biases, affect people’s judgements of minority- and
majority-group sizes18.
It has been observed that social perception biases can be related
to the structural properties of personal networks19,20, which can
strongly affect the samples of information on which individuals rely
when forming their social perceptions21,22. However, the impact of
different network properties on social perception biases has not yet
been systematically explored. Here we explore three such proper-
ties. The first is the level of homophily, or how likely the one is to
be connected to similar others, which is known as a fundamental
structural property of many social networks23. The second property
is the asymmetry of homophily, or whether homophily is larger in
some subgroups than in others. For example, it has been observed
that in scientific collaborations, homophily among women is stron-
ger than homophily among men24. The third property is the rela-
tive size of minority and majority groups in the society. Many social
networks are characterized by a large majority group and a much
smaller minority group. Examples are the proportions of different
genders in science, technology, engineering and maths, of people
with different levels of income and of people who smoke or not.
Most existing explanations of social perception biases invoke
motivational and cognitive processes rather than social network
structure. For example, processes that explain overestimation of
the frequency of ones own attributes (for example, false consensus)
include wishful thinking25, easier recall of the reasons for having
one’s own view9, rational inference of population frequencies based
on ones own attributes26, feeling good when others share one’s own
view27, and justifying one’s undesirable behaviours by overestimat-
ing their frequency in society28. However, these processes cannot
explain the opposite effect, underestimating the frequency of our
own view (for example, false uniqueness). Instead, this opposite bias
is typically explained by a different set of cognitive or motivational
processes, such as differential attention to one’s own and other
groups13 and bolstering perceived self-competence14. Ideally, both
overestimation and underestimation biases would be explained by
a single mechanism18.
Here we show empirically, analytically and numerically that a
simple network model can explain both over- and underestimation
in social perceptions, without assuming biased motivational or cog-
nitive processes. Results from a cross-cultural survey show that the
level of homophily and size of the minority group influence people’s
social perception biases. Analytical results from a generative net-
work model with tunable homophily and minority-group size align
well with the empirical findings. Numerical investigations show that
Homophily and minority-group size explain
perception biases in social networks
Eun Lee 1,11*, Fariba Karimi 2,3,11*, Claudia Wagner 2,3, Hang-Hyun Jo 4,5,6, Markus Strohmaier 2,7
and Mirta Galesic 8,9,10
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.
NATURE HUMAN BEHAVIOUR | VOL 3 | OCTOBER 2019 | 1078–1087 | www.nature.com/nathumbehav
1078
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... Information transmission in the context of Information and Communication Technologies is a great opportunity to create a better-informed society, but in practice, these technologies are also promoting phenomena such as the viral spreading of fake news 1-3 , echo chambers 4-6 , perception biases like false consensus or majority illusions 7 and social polarization 5,6,8 . We understand by echo chambers situations in which the transmission of information among individuals belonging to the same opinion group is dominant, while transmission among individuals with different opinions is hindered. ...
... The real social impact of echo chambers and their causal link with misinformation cascades are debated topics [9][10][11] , but data-driven and computational approaches confirm that the structural properties of social networks are tied to the emergence of echo chambers [4][5][6] . In particular, the homophily of the network -that is, the tendency of nodes to be connected to other nodes of the same group-seems to be a key ingredient to generate echo chambers 5 and perception biases 7 . ...
... Changing the degrees of the source and target nodes does not cause novel behaviors, so they can be disregarded as control parameters (not shown). Importantly, this simplified model does not account for the observed strong biases in the presence of homophily 4,7 . ...
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