The "Majority Illusion" in Social Networks

By Kristina Lerman, Xiaoran Yan, Xin-Zeng Wu. 2015

226 citations recorded by the source corpus.

Individual’s decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual’s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the “majority illusion,” leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “On social platforms, they may also result in systematically biased perceptions about users or content (Lerman et al., 2016).”
Consumers' self-congruence with a 'Liked' brand: cognitive network influence and brand outcomes (2017, background): “ideas in social networks (Lerman et al., 2015), we suggest future research into attitude homophily consider the ‘majority illusion’, by also exploring the degree distribution of the network and its influence on the observations of others.”
Political Polarization on the Digital Sphere: A Cross-platform, Over-time Analysis of Interactional, Positional, and Affective Polarization on Social Media (2020, background): “Confirmed time and again in homophilic interactions (Slater, 2007), users’ prior beliefs may be increasingly removed from possible doubt or reservation (Lerman et al., 2016), eroding their tolerance of competing views.”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “Moreover, it would require users to correctly estimate the popularity of diﬀerent pieces of information in their local neighborhood, a measurement that is easily skewed in networks [23].”