Information Overload and Viral Marketing: Countermeasures and Strategies

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

10 Pages Posted: 16 Nov 2010

See all articles by Jiesi Cheng

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

Date Written: October, 20 2009

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.

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: https://ssrn.com/abstract=1491850

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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