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Rumour Propagation on Social Networks as a Function of Diversity

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Advanced Dynamic Modeling of Economic and Social Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 448))

Abstract

In the case of a rumour propagating across a social network comprised of two interconnected groups, a majority group and a minority group, the effect on the rumour propagation of the minority’s distribution in the social network is investigated. The rumour is derogatory towards the minority group and its transmission is simulated using the GBN-Dialogue model of rumour propagation on realistic social networks. The integration of the minority group into the entire social network is measured by the Minority Integration Metric (MIM). Monte Carlo simulations show that rumour penetration into the minority subgroup is an increasing linear function of the MIM and thus minority members of the social network will have a higher level of belief in the minority-derogatory rumour if the minority is more integrated into the social network. A threshold MIM value can be estimated below which the rumour fails to penetrate the minority subgroup.

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References

  1. The Secret History of 9/11: Terrorist Threats Ignored. Canadian Broadcasting Corporation. September 10 (2006)

    Google Scholar 

  2. DiFonzo, N.: The Watercooler Effect: A Psychologist Explores the Extraordinary Power of Rumors. Avery (Penguin), New York (2008), http://www.thewatercoolereffect.com

    Google Scholar 

  3. Bordia, P., DiFonzo, N.: Psychological motivations in rumor spread. In: Fine, G.A., Heath, C., Campion-Vincent, V. (eds.) Rumor Mills: The Social Impact of Rumor and Legend, pp. 87–101. Aldine Press, NY (2005)

    Google Scholar 

  4. Brooks, B.P., DiFonzo, N., Ross, D.S.: The GBN-Dialogue Model of Outgroup-Negative Rumor Transmission: Group Membership, Belief, and Novelty (2011) (in press)

    Google Scholar 

  5. Longo, D., Brooks, B.P.: Modeling the RIT Facebook Social Network. Presented to the Centre for Applied and Computational Mathematics at the Rochester Institute of Technology (2008)

    Google Scholar 

  6. Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 409–410 (1998)

    Article  Google Scholar 

  7. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)

    Article  MATH  Google Scholar 

  8. Burgers, K.M., Upton, J., Brooks, B.P.: Generating Networks with Target Clustering and Path Length. 2010 NSF-REU in Extremal Graph Theory and Dynamical Systems. School of Mathematical Sciences. Rochester Institute of Technology (2010)

    Google Scholar 

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Brooks, B. (2013). Rumour Propagation on Social Networks as a Function of Diversity. In: Proto, A., Squillante, M., Kacprzyk, J. (eds) Advanced Dynamic Modeling of Economic and Social Systems. Studies in Computational Intelligence, vol 448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32903-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-32903-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32902-9

  • Online ISBN: 978-3-642-32903-6

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