Abstract

http://ssrn.com/abstract=1491850
 


 



Information Overload and Viral Marketing: Countermeasures and Strategies


Jiesi Cheng


University of Arizona - Department of Management Information Systems

Aaron R. Sun


University of Arizona - Department of Management Information Systems

Daniel Dajun Zeng


University of Arizona - Department of Management Information Systems

October, 20 2009

International Conference on Social Computing, Behavioral Modeling, & Prediction (SBP10), 2010

Abstract:     
Studying information diffusion through social networks has become an active research topic with important implications in viral marketing applications. One of the fundamental algorithmic problems related to viral marketing is the Influence Maximization (IM) problem: given a social network, which set of nodes should be considered by the viral marketer as the initial targets, in order to maximize the influence of the advertising message. In this work, we study the IM problem in an information-overloaded online social network. Information overload occurs when individuals receive more information than they can process, which can cause negative impacts on the overall marketing effectiveness. Many practical countermeasures have been proposed for alleviating the load of information on recipients. However, how these approaches can benefit viral marketers is not well understood. In our work, we have adapted the classic Information Cascade Model to incorporate information overload and study its countermeasures. Our results suggest that effective control of information overload has the potential to improve marketing effectiveness, but the targeting strategy should be re-designed in response to these countermeasures.

Number of Pages in PDF File: 10

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Date posted: November 16, 2010  

Suggested Citation

Cheng, Jiesi and Sun, Aaron R. and Zeng, Daniel Dajun, Information Overload and Viral Marketing: Countermeasures and Strategies (October, 20 2009). International Conference on Social Computing, Behavioral Modeling, & Prediction (SBP10), 2010. Available at SSRN: http://ssrn.com/abstract=1491850

Contact Information

Jiesi Cheng (Contact Author)
University of Arizona - Department of Management Information Systems ( email )
AZ
United States
Aaron R. Sun
University of Arizona - Department of Management Information Systems ( email )
AZ
United States
Daniel Dajun Zeng
University of Arizona - Department of Management Information Systems ( email )
AZ
United States
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