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Service Adoption Spreading in Online Social Networks

  • Gerardo Iñiguez
  • Zhongyuan Ruan
  • Kimmo Kaski
  • János Kertész
  • Márton Karsai
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

The collective behaviour of people adopting an innovation, product or online service is commonly interpreted as a spreading phenomenon throughout the fabric of society. This process is arguably driven by social influence, social learning and by external effects like media. Observations of such processes date back to the seminal studies by Rogers and Bass, and their mathematical modelling has taken two directions: One paradigm, called simple contagion, identifies adoption spreading with an epidemic process. The other one, named complex contagion, is concerned with behavioural thresholds and successfully explains the emergence of large cascades of adoption resulting in a rapid spreading often seen in empirical data. The observation of real-world adoption processes has become easier lately due to the availability of large digital social network and behavioural datasets. This has allowed simultaneous study of network structures and dynamics of online service adoption, shedding light on the mechanisms and external effects that influence the temporal evolution of behavioural or innovation adoption. These advancements have induced the development of more realistic models of social spreading phenomena, which in turn have provided remarkably good predictions of various empirical adoption processes. In this chapter we review recent data-driven studies addressing real-world service adoption processes. Our studies provide the first detailed empirical evidence of a heterogeneous threshold distribution in adoption. We also describe the modelling of such phenomena with formal methods and data-driven simulations. Our objective is to understand the effects of identified social mechanisms on service adoption spreading, and to provide potential new directions and open questions for future research.

Notes

Acknowledgements

The results presented in this chapter are adapted from [69, 70] and were obtained in collaboration with Riivo Kikas. The authors gratefully acknowledge the support of M. Dumas, A. Saabas, and A. Dumitras from STACC and Microsoft/Skype Labs. GI acknowledges a Visiting Fellowship from the Aalto Science Institute. JK and ZR were supported by FP7 317532 Multiplex and JK by H2020 FETPROACT-GSS CIMPLEX 641191. KK is supported by the Academy of Finland’s project COSDYN project, No. 276439 and EU HORIZON 2020 FET Open RIA IBSEN project No. 662725.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gerardo Iñiguez
    • 1
    • 2
  • Zhongyuan Ruan
    • 3
  • Kimmo Kaski
    • 2
  • János Kertész
    • 3
  • Márton Karsai
    • 4
  1. 1.Institute for Research in Applied Mathematics and SystemsNational Autonomous University of MexicoMéxicoMexico
  2. 2.Department of Computer ScienceAalto University School of ScienceAaltoFinland
  3. 3.Center for Network ScienceCentral European UniversityBudapestHungary
  4. 4.Univ de Lyon, ENS de Lyon, INRIA, CNRS, UMR 5668, IXXILyonFrance

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