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The “majority illusion” in social networks
Open accessAbstract
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.
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“homophily—the tendency of similar people to interact and connect (McPherson et al., 2001)—can systematically bias the perceptions of networked users, resulting in under- or over-estimations of the prevalence of user attributes within a population”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lerman et al., 2016
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“On social platforms, they may also result in systematically biased perceptions about users or content”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lerman et al., 2016
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Metadata
- Authors
- Lerman, K., Yan, X., & Wu, X.-Z.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Lerman, Kristina
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-17
- Volume
- 11
- Issue
- 2
- Pages
- e0147617
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4757419
- OpenAlex
- W1552468722
- Cited by
- 669
- References
- 51
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 15 · 2023: 26 · 2022: 22 · 2021: 135 · 2020: 123 · 2019: 119 · 2018: 94 · 2017: 78 · 2016: 30
- FWCI
- 60.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1217605)
- Defense Advanced Research Projects Agency (W911NF- 12-1-0034)
- Advanced Research Projects Agency
- Air Force Office of Scientific Research (FA9550-, FA9550-10-1-, FA9550-10-1-0569)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- social networks, prevalence estimation, social contagion, network structure, degree heterogeneity, disassortative mixing, statistical models
- MeSH terms
- Humans, Illusions, Normal Distribution, Social Networking
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