A Marketing Game: A Model for Social Media Mining and Manipulation

  • Matthew G. ReyesEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)


This paper derives marketing-influenced Glauber dynamics for socially-contingent consumer choice, which rests on the foundation of socially-contingent random utility. This dynamics model provides companies with a reinforcement learning approach to influencing consumer decision-making. The paper presents a procedure for using machine learning algorithms to estimate consumer preferences as well as direct and social biases on the network. The paper discusses the use of market research to estimate inherent biases and marketing responses for individual consumers. Finally, the paper illustrates on a star-chain network how optimization of marketing allocation depends on parameter estimation.


  1. 1.
    Abebe, R., Kleinberg, J., Parkes, D., Tsourakakis, C.E.: Opinion dynamics with varying susceptibility to persuasion. In: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), KDD 2018, London, UK, August 2018Google Scholar
  2. 2.
    Barabasi, A.-L., Bonabeau, E.: Scale-free networks. Sci. Am. 288, 60–69 (2003)CrossRefGoogle Scholar
  3. 3.
    Bell, D.E., Keeney, R.L., Little, J.D.C.: A market share theorem. J. Mark. Res. 12(2), 136–141 (1975)CrossRefGoogle Scholar
  4. 4.
    Blume, L.E.: Statistical mechanics of strategic interaction. Games Econ. Behav. 5(3), 387–424 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Block, H.D., Marschak, J.: Random orderings and stochastic theories of responses. In: Contributions to Probability and Statistics. Stanford University Press (1960)Google Scholar
  6. 6.
    Britt, S.H.: How Weber’s Law can be applied to marketing. Bus. Horiz. 18(1), 21–29 (1975)CrossRefGoogle Scholar
  7. 7.
    Brock, W.A., Durflauf, S.N.: Discrete choice with social interactions. Rev. Econ. Stud. 68, 235–260 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)CrossRefGoogle Scholar
  9. 9.
    Chierichetti, F., Kleinberg, J., Oren, S.: On discrete preferences and coordination. In: ACM Conference on Electronic Commerce, pp. 233–250 (2013)Google Scholar
  10. 10.
    De, A., Bhattacharya, S., Bhattacharya, P., Ganguly, N., Chakrabarti, S.: Learning a linear influence model from transient opinion dynamics. In: The 23rd ACM International Conference on Information and Knowledge Management, pp. 401–410. ACM (2014)Google Scholar
  11. 11.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of SIGKDD 2001, San Franciso, CA, pp. 57–66 (2001)Google Scholar
  13. 13.
    Ellison, G.: Learning, local interaction, and coordination. Econometrica 61(5), 1047–1071 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Fazeli, A., Jadbabaie, A.: Game Theoretic Analysis of a Strategic Model of Competitive Contagion and Product Adoption in Social Networks, December 2012.
  15. 15.
    Gladwell, M.: The Tipping Point, Little, Brown and Company (2000)Google Scholar
  16. 16.
    Glauber, R.J.: Time-dependent statistics of the Ising model. J. Math. Phys. 4, 294–307 (1963)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  18. 18.
    Green, P.E., Carmone, F.J., Wachspress, D.P.: On the analysis of qualitative data in marketing research. J. Mark. Res. 14, 52–59 (1977)CrossRefGoogle Scholar
  19. 19.
    Kempe, D., Kleinberg, J., Tardos, E.: Influential nodes in a diffusion model for social networks. In: Proceedings of the 32nd International Conference on Automata, Languages, and Programming (ICALP), pp. 1127–1138 (2005)CrossRefGoogle Scholar
  20. 20.
    Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 278 (1979)zbMATHCrossRefGoogle Scholar
  21. 21.
    Kim, D.-H., Noh, J.D., Jeong, H.: Scale-free trees: the skeletons of complex networks. Phys. Rev. E 70, 046126 (2004)CrossRefGoogle Scholar
  22. 22.
    Lasswell, H.D.: The structure and function of communication in society. Bryson, L. (ed.) The Communication of Ideas. Harper and Brothers (1948)Google Scholar
  23. 23.
    Lazarsfield, P.F., Berelson, B., Gaudet, H.: The People’s Choice: How the Voter Makes up his Mind in a Presidential Campaign. Columbia University Press, New York City (1944)Google Scholar
  24. 24.
    Luce, D.: Individual Choice Behavior. Dover, Illinois (1959)zbMATHGoogle Scholar
  25. 25.
    Malhotra, N.K.: The use of linear logit models in marketing research. J. Mark. Res. 21, 20–31 (1984)MathSciNetCrossRefGoogle Scholar
  26. 26.
    McFadden, D.: Conditional logit analysis of qualitative choice behavior. In: Frontiers in Econometrics. Academic Press, New York (1974)Google Scholar
  27. 27.
    Montanari, A., Saberi, A.: The spread of innovations in social networks. PNAS 107(47), 20196–20201 (2010)CrossRefGoogle Scholar
  28. 28.
    Morris, S.: Contagion. Rev. Econ. Stud. 67, 57–78 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Ogilvy, D.: Ogilvy On Advertising. Vintage, New York City (1985)Google Scholar
  30. 30.
    Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015)CrossRefGoogle Scholar
  31. 31.
    Reyes, A., Rosso, R., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)CrossRefGoogle Scholar
  32. 32.
    Reyes, M.G., Neuhoff, D.L.: Minimum conditional description length estimation of Markov random fields. In: Information Theory and Applications Workshop, February 2016Google Scholar
  33. 33.
    Reyes, M.G.: A marketing game: a rigorous model for strategic resource allocation. In: ACM Workshop on Machine Learning in Graphs, London, UK, August 2018Google Scholar
  34. 34.
    Richardson, M., Domingos, P.: Mining the network value of customers. In: Proceedings of SIGKDD, Edmonton, Alberta, Canada (2002)Google Scholar
  35. 35.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99–118 (1955)CrossRefGoogle Scholar
  36. 36.
    Stevens, S.S.: To honor Fechner and repeal his law. Science 133(3446), 80–86 (1961)CrossRefGoogle Scholar
  37. 37.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  38. 38.
    Train, K.: Discrete Choice Models with Simulation. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  39. 39.
    Thurstone, L.L.: Psychological analysis. Am. J. Psychol. 38(3), 368–389 (1927)CrossRefGoogle Scholar
  40. 40.
    Tversky, A.: Elimination by aspects: a theory of choice. Psychol. Rev. 79(4), 281 (1972)CrossRefGoogle Scholar
  41. 41.
    Wainwright, M., Jordan, M.: Graphical models, exponential families, and variational inference. Technical report, UC Berkeley (2003)Google Scholar
  42. 42.
    Wainwright, M.J.: Estimating the “wrong” graphical model: benefits in the computation-limited setting. J. Mach. Learn. Res. 7, 1829–1859 (2006)MathSciNetzbMATHGoogle Scholar
  43. 43.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440 (1998)zbMATHCrossRefGoogle Scholar
  44. 44.
    Watts, D.J., Dodds, P.S.: Influentials, networks, and public opinion formation. J. Consum. Res. 34, 441–458 (2007)CrossRefGoogle Scholar
  45. 45.
    Young, H.P.: The evolution of conventions. Econometrica 61(1), 57–84 (1993)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Independent Researcher and ConsultantAnn ArborUSA

Personalised recommendations