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Home»Articles»A Study of Viral Marketing on Social Network in Stream Line Business Model of Data Mining in Management

A Study of Viral Marketing on Social Network in Stream Line Business Model of Data Mining in Management

Author : S. Muthumari
Volume 8 No.1 Special Issue:February 2019 pp 84-86

Abstract

Viral marketing and viral advertising refer to marketing techniques that use pre-existing social networks to produce exponential increases in brand awareness, through self-replicating viral processes, analogous to the spread of a computer virus. It can be often word-of-mouth delivered and enhanced online harness the network effect of the Internet and is very useful in reaching a large number of people very fast. The ultimate goal of marketers interested in creating successful viral marketing programs is to create viral messages that appeal to individuals with high social networking potential (SNP) and that have a high probability of being presented and spread by these individuals and their competitors in their communications with others in a short period of time. There are several sources of social networks where our model can be applied to, and a few are: Facebook, Twitter, LinkedIn, YouTube, Google+, Instagrams, online forums, email mailing lists.

Keywords

Viral Marketing,Social Networks, Viral Online Marketing, Internet, Viral Marketing Strategies & Advertising Service, Business Model, Data Mining

Full Text:

References

[1] P. Dolningos and M. Richardson, “Mining the Network Value of Customers”, In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, ACM Press, pp. 57-66, 2001.
[2] A. M. Hughes, “The Complete Database Marketer: Second Generation Strategies and Techniques for Tapping the Power of you Customer Database”, Irwin, Chicago, IL, 1996.
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[5] S. Wasserman and K. Faust, “Social Network Analysis: Methods and Applications”, Cambridge University Press, Cambridge, UK, 1994.
[6] S. Jurvetson, “What exactly is viral marketing? Red Herring”, Vol. 78, pp. 110-112, 2000.
[7] Mining Knowledge-Sharing Sites for Viral Marketing Matthew Richardson and Pedro Domingos Department of Computer Science and Engineering University of Washington Box 352350 Seattle, WA 98195-2350 {mattr, pedrod} @ cs.washington.edu.
[8] Vasu Deva: E-marketing For Excellence, 2003.
[9] Vasu Deva: E-Business: search for Excellence, 2003.
[10] P. D. Saini, Marketing in 21st Century, 2010
[11] Mohammed Rafi A. & Fisher, Robert J: Internet Marketing: Building avantagec in a Networked Economy, 2001.
[12] [Online] Available: www.facebook .com
[13] [Online] Available: www.myspace.info
[14] [Online] Available: www.crome.info
[15] M. Richardson and P. Domingos, “Mining Knowledge-Sharing Sites for Viral Marketing”, SIGKDD02, Edmonton, Alberta, Canada, ACM, pp. 61-70, 2002.
[16] M. R. Subramani and P. Rajagopalan, “Knowledge-sharing and Influence in online Social Networks via Viral Marketing”, Communications of the ACM, Vol. 46, No. 12, 2003.
[17] S. Banerjee, H. A. Qaheri, and A. E. Hassanien, “Mining Social Networks for Viral Marketing Using Fuzzy Logic”, Computer Society Fourth Asia International Conference on Mathematical/Analytical Modeling and Computer Simulation,IEEE, pp. 24-28, 2010.
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[20] S. Y. Bhat and M. Abulaish, “Overlapping Social Network Communicates and Viral Marketing”, International Symposium on Computational and Business Intelligence, IEEE, pp.243-246, 2013.
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Viral marketing and viral advertising refer to marketing techniques that use pre-existing social networks to produce exponential increases in brand awareness, through self-replicating viral processes, analogous to the spread of a computer virus. It can be often word-of-mouth delivered and enhanced online harness the network effect of the Internet and is very useful in reaching a large number of people very fast. The ultimate goal of marketers interested in creating successful viral marketing programs is to create viral messages that appeal to individuals with high social networking potential (SNP) and that have a high probability of being presented and spread by these individuals and their competitors in their communications with others in a short period of time. There are several sources of social networks where our model can be applied to, and a few are: Facebook, Twitter, LinkedIn, YouTube, Google+, Instagrams, online forums, email mailing lists.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Table of Contents

    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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