Information overload and viral marketing: Countermeasures and strategies

Jiesi Cheng, Aaron Sun, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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 an 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.

Original languageEnglish (US)
Title of host publicationAdvances in Social Computing - Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, Proceedings
Pages108-117
Number of pages10
DOIs
StatePublished - Dec 24 2010
Event3rd International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010 - Bethesda, MD, United States
Duration: Mar 30 2010Mar 31 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6007 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010
CountryUnited States
CityBethesda, MD
Period3/30/103/31/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Cheng, J., Sun, A., & Zeng, D. (2010). Information overload and viral marketing: Countermeasures and strategies. In Advances in Social Computing - Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, Proceedings (pp. 108-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6007 LNCS). https://doi.org/10.1007/978-3-642-12079-4_16