Evidential positive opinion influence measures for viral marketing

  • Siwar JendoubiEmail author
  • Arnaud Martin
Regular Paper


The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinion-based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produces effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real-world dataset collected from Twitter.


Influence maximization Influence measure User opinion Theory of belief functions Viral marketing 



  1. 1.
    Baccianella S, Esuli A, Sebatiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, pp 2200–2204Google Scholar
  2. 2.
    Barbieri N, Bonchi F, Manco G (2013) Topic-aware social influence propagation models. Knowl Inf Syst 37(3):555–584CrossRefGoogle Scholar
  3. 3.
    Baumeister R, Bratslavsky E, Finkenauer C, Vohs K (2001) Bad is stronger than good. Rev Gen Psychol 5(4):323–370CrossRefGoogle Scholar
  4. 4.
    Chen D, Lü L, Shang MS, Zhang YC, Zhou T (2012) Identifying influential nodes in complex networks. Physica A Stat Mech Appl 391(4):1777–1787CrossRefGoogle Scholar
  5. 5.
    Chen W, Collins A, Cummings R, Ke T, Liu Z, Rincon D, Sun X, Wang Y, Wei W, Yuan Y (2011) Influence maximization in social networks when negative opinions may emerge and propagate. In: Proceedings of SIAM SDM, pp 379–390Google Scholar
  6. 6.
    Cheung CM, Lee MK (2008) Online consumer reviews: does negative electronic word-of-mouth hurt more? In: Proceeding of the fourteenth Americas conference on information systems, p 143Google Scholar
  7. 7.
    Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325–339MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of KDD’01, pp 57–66Google Scholar
  9. 9.
    Gao C, Wei D, Hu Y, Mahadevan S, Deng Y (2013) A modified evidential methodology of identifying influential nodes in weighted networks. Physica A 392(21):5490–5500MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223CrossRefGoogle Scholar
  11. 11.
    Goyal A, Bonchi F, Lakshmanan LVS (2012) A data-based approach to social influence maximization. In: Proceedings of VLDB endowment, pp 73–84Google Scholar
  12. 12.
    Granovetter M (1978) Threshold models of collective behavior. Am J Soc 83:1420–1443 CrossRefGoogle Scholar
  13. 13.
    Jendoubi S, Chebbah M, Martin A (2018) Evidential independence maximization on twitter network. In: Destercke S, Denoeux T, Cuzzolin F, Martin A (eds) Belief functions: theory and applications. Springer International Publishing, Compiègne, France, pp 121–128 Google Scholar
  14. 14.
    Jendoubi S, Martin A, Liétard L, Ben Hadj H, Ben Yaghlane B (2016) Maximizing positive opinion influence using an evidential approach. In: Poceeding of the 12th international FLINS conferenceGoogle Scholar
  15. 15.
    Jendoubi S, Martin A, Liétard L, Ben Hadj H, Ben Yaghlane B (2017) Two evidential data based models for influence maximization in twitter. Knowl Based Syst 121:58–70CrossRefGoogle Scholar
  16. 16.
    Jendoubi S, Martin A, Liétard L, Ben Yaghlane B (2014) Classification of message spreading in a heterogeneous social network. In: Proceeding of IPMU, pp 66–75Google Scholar
  17. 17.
    Jendoubi S, Martin A, Liétard L, Ben Yaghlane B, Ben Hadj H (2015) Dynamic time warping distance for message propagation classification in twitter. In: Proceeding of ECSQARU, pp 419–428Google Scholar
  18. 18.
    Jurvetson S (2000) What exactly is viral marketing? Red Herring 78:110–112Google Scholar
  19. 19.
    Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of KDD’03, pp 137–146Google Scholar
  20. 20.
    Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of KDD’07, pp 420–429Google Scholar
  21. 21.
    Li D, Xu ZM, Chakraborty N, Gupta A, Sycara K, Li S (2014) Polarity related influence maximization in signed social networks. PLoS ONE 9(7):e102199CrossRefGoogle Scholar
  22. 22.
    Li YM, Lai CY, Chen CW (2011) Discovering influencers for marketing in the blogosphere. Inf Sci 181(23):5143–5157CrossRefGoogle Scholar
  23. 23.
    Liu Q, Xiang B, Yuan NJ, Chen E, Xiong H, Zheng Y, Yang Y (2017) An influence propagation view of PageRank. ACM Trans Knowl Discov Data 11(3):1–30Google Scholar
  24. 24.
    Moosavi SA, Jalali M, Misaghian N, Shamshirband S, Anisi MH (2017) Community detection in social networks using user frequent pattern mining. Knowl Inf Syst 51(1):159–186CrossRefGoogle Scholar
  25. 25.
    Narayanam R, Nanavati AA (2014) Design of viral marketing strategies for product cross-sell through social networks. Knowl Inf Syst 39(3):609–641CrossRefGoogle Scholar
  26. 26.
    Newman MEJ (2010) Networks: an introduction. Oxford University Press, OxfordCrossRefzbMATHGoogle Scholar
  27. 27.
    Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonzbMATHGoogle Scholar
  28. 28.
    Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66:191–234MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Taylor SE (1991) Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychol Bull 1(110):67–85CrossRefGoogle Scholar
  30. 30.
    Wei D, Deng X, Zhang X, Deng Y, Mahadeven S (2013) Identifying influential nodes in weighted networks based on evidence theory. Physica A 392(10):2564–2575CrossRefGoogle Scholar
  31. 31.
    Xiang B, Liu Q, Chen E, Xiong H, Zheng Y, Yang Y (2013) PageRank with priors: an influence propagation perspective. In: Proceedings of the twenty-third international joint conference on artificial intelligence, pp 2740–2746Google Scholar
  32. 32.
    Yang J, Liu C, Teng M, Chen J, Xiong H (2018) A unified view of social and temporal modeling for B2B marketing campaign recommendation. IEEE Trans Knowl Data Eng 30(5):810–823CrossRefGoogle Scholar
  33. 33.
    Zhang H, Dinh TN, Thai MT (2013) Maximizing the spread of positive influence in online social networks. In: Proceedings of ICDCS, pp 317–326Google Scholar
  34. 34.
    Zhou K, Martin A, Pan Q, Liu ZG (2015) Median evidential C-means algorithm and its application to community detection. Knowl Based Syst 74:69–88CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LARODEC, ISG TunisUniversity of TunisLe BardoTunisia
  2. 2.DRUID, Univ Rennes, CNRA, IRISALannionFrance

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