Incentivized Social Sharing: Characteristics and Optimization

  • Joseph J. PfeifferIII
  • Elena ZhelevaEmail author
Part of the Lecture Notes in Social Networks book series (LNSN)


Many modern digital platforms provide features for social sharing, thus increasing the reach of their products and services through person-to-person recommendations. Meanwhile, the question how monetary incentives for social sharing affect the behavior of users remains largely unexplored. The effectiveness of incentives is often hard to optimize due to the large space of incentive parameters and the inherent tradeoff between the incentive attractiveness for the customer and the return on investment for the company. Here, we present a case study in which we show how incentives can change the structural properties of social networks and shift the power-law curve of sharing. We distinguish between altruistic and incentivized shares, and we look at the impact of different incentive amounts on the sharing behavior of users. We propose a novel graph evolution model, Me+N model, which provides flexibility in exploring the effect of different incentive parameters on company’s profits by capturing the probabilistic nature of customer behavior over time. We look at a specific family of incentives in which customers get a reward if they convince a minimum, pre-specified number of friends to purchase a given product. Our analysis shows that simple monetary incentives can be surprisingly effective in social media strategies.


  1. 1.
    N. Agarwal, H. Liu, L. Tang, P.S. Yu, Identifying the influential bloggers in a community, in WSDM (2008)Google Scholar
  2. 2.
    A. Anagnostopoulos, G. Brova, E. Terzi, Peer and authority pressure in information-propagation models, in PKDD (2011)Google Scholar
  3. 3.
    S. Aral, D. Walker, Identifying influential and susceptible members of social networks. Science 337, 337–341 (2012)CrossRefGoogle Scholar
  4. 4.
    E. Bakshy, J.M. Hofman, W.A. Mason, D.J. Watts, Everyone’s an influencer: quantifying influence on twitter, in WSDM (2011)CrossRefGoogle Scholar
  5. 5.
    A. Barabasi, R. Albert, Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefGoogle Scholar
  6. 6.
    D. Chakrabarti, C. Faloutsos, Graph Mining: Laws, Tools, and Case Studies. Synthesis Lectures on Data Mining and Knowledge Discovery (Morgan & Claypool Publishers, San Rafael, 2012)Google Scholar
  7. 7.
    W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, in KDD (2009)Google Scholar
  8. 8.
    A. Clauset, C. Shalizi, M. Newman, Power-law distributions in empirical data. SIAM 51(4), 661–703 (2009)CrossRefGoogle Scholar
  9. 9.
    P. Domingos, M. Richardson, Mining the network value of customers, in KDD (2001)Google Scholar
  10. 10.
    R. Durrett, Essentials of Stochastic Processes. Springer Texts in statistics (Springer, New York, 2012)Google Scholar
  11. 11.
    P. Golle, K. Leyton-Brown, I. Mironov, M. Lillibridge, Incentives for sharing in peer-to-peer networks. Electron. Commer. 2232, 75–87 (2001)CrossRefGoogle Scholar
  12. 12.
    N. Immorlica, V. Mirrokni, Tutorial: optimal marketing and pricing in social networks, in WWW (2010)Google Scholar
  13. 13.
    D. Kempe, J. Kleinberg, E. Tardos, Maximizing the spread of influence through a social network, in KDD (2003)Google Scholar
  14. 14.
    L. Kornish, Q. Li, Optimal referral bonuses with asymmetric information: Firm-offered and interpersonal incentives. Market. Sci. 29(1), 108–121 (2010)CrossRefGoogle Scholar
  15. 15.
    T. Lappas, E. Terzi, Daily-deal selection for revenue maximization, in CIKM (2012)Google Scholar
  16. 16.
    S. Lattanzi, D. Sivakumar, Affiliation networks, in STOC (June 2009)Google Scholar
  17. 17.
    J. Leskovec, J. Kleinberg, C. Faloutsos, Graphs over time: densification laws, shrinking diameters and possible explanations. Trans. Knowl. Discov. Data 3(2), 177–187 (2005)Google Scholar
  18. 18.
    J. Leskovec, L.A. Adamic, B.A. Huberman, The dynamics of viral marketing. ACM Trans. Web 1(1) (2007)Google Scholar
  19. 19.
    J. Leskovec, L. Backstrom, R. Kumar, A. Tomkins, Microscopic evolution of social networks, in KDD (2008)Google Scholar
  20. 20.
    J.J. Pfeiffer III, E. Zheleva, Incentivized sharing in social networks, in VLDB Workshop on Online Social Systems (WOSS) (2012)Google Scholar
  21. 21.
    J.J. Pfeiffer III, E. Zheleva, Optimizing the effectiveness of incentivized social sharing, in ASONAM (2017)Google Scholar
  22. 22.
    M. Salganik, P. Dodds, D. Watts, Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762), 854–856 (2006)CrossRefGoogle Scholar
  23. 23.
    H. Sharara, W. Rand, L. Getoor, Differential adaptive diffusion: understanding diversity and learning whom to trust in viral marketing, in ICWSM (2011)Google Scholar
  24. 24.
    M. Ye, C. Wang, C. Aperjis, B.A. Huberman, T. Sandholm, Collective attention and the dynamics of group deals, in WWW (2012)Google Scholar
  25. 25.
    E. Zheleva, H. Sharara, L. Getoor, Co-evolution of social and affiliation networks, in KDD (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.MicrosoftSeattleUSA
  2. 2.University of Illinois at ChicagoChicagoUSA

Personalised recommendations