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Open AccessArticle

Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

1
Department of Mathematics & Statistics, University of Vermont, Burlington, VT 05405, USA
2
School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(3), 265; https://doi.org/10.3390/e22030265
Received: 1 February 2020 / Revised: 21 February 2020 / Accepted: 24 February 2020 / Published: 26 February 2020
(This article belongs to the Special Issue Computation in Complex Networks)
Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior. View Full-Text
Keywords: online social networks; social media; information spreading; information diffusion; cross-entropy online social networks; social media; information spreading; information diffusion; cross-entropy
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Pond, T.; Magsarjav, S.; South, T.; Mitchell, L.; Bagrow, J.P. Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow. Entropy 2020, 22, 265.

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