Abstract
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.
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References
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 2200–2204
Barbieri N, Bonchi F, Manco G (2013) Topic-aware social influence propagation models. Knowl Inf Syst 37(3):555–584
Baumeister R, Bratslavsky E, Finkenauer C, Vohs K (2001) Bad is stronger than good. Rev Gen Psychol 5(4):323–370
Chen D, Lü L, Shang MS, Zhang YC, Zhou T (2012) Identifying influential nodes in complex networks. Physica A Stat Mech Appl 391(4):1777–1787
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 379–390
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 143
Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325–339
Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of KDD’01, pp 57–66
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):5490–5500
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):211–223
Goyal A, Bonchi F, Lakshmanan LVS (2012) A data-based approach to social influence maximization. In: Proceedings of VLDB endowment, pp 73–84
Granovetter M (1978) Threshold models of collective behavior. Am J Soc 83:1420–1443
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
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 conference
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 121:58–70
Jendoubi S, Martin A, Liétard L, Ben Yaghlane B (2014) Classification of message spreading in a heterogeneous social network. In: Proceeding of IPMU, pp 66–75
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 419–428
Jurvetson S (2000) What exactly is viral marketing? Red Herring 78:110–112
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of KDD’03, pp 137–146
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 420–429
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):e102199
Li YM, Lai CY, Chen CW (2011) Discovering influencers for marketing in the blogosphere. Inf Sci 181(23):5143–5157
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):1–30
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):159–186
Narayanam R, Nanavati AA (2014) Design of viral marketing strategies for product cross-sell through social networks. Knowl Inf Syst 39(3):609–641
Newman MEJ (2010) Networks: an introduction. Oxford University Press, Oxford
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton
Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66:191–234
Taylor SE (1991) Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychol Bull 1(110):67–85
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):2564–2575
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 2740–2746
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):810–823
Zhang H, Dinh TN, Thai MT (2013) Maximizing the spread of positive influence in online social networks. In: Proceedings of ICDCS, pp 317–326
Zhou K, Martin A, Pan Q, Liu ZG (2015) Median evidential C-means algorithm and its application to community detection. Knowl Based Syst 74:69–88
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Jendoubi, S., Martin, A. Evidential positive opinion influence measures for viral marketing. Knowl Inf Syst 62, 1037–1062 (2020). https://doi.org/10.1007/s10115-019-01375-w
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DOI: https://doi.org/10.1007/s10115-019-01375-w