Advertisement

Burst Moment Estimation for Information Propagation

  • Tomas Kuzar
  • Pavol Navrat
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 67)

Abstract

In the article we concentrate on timing aspect of information propagation on social web. The aim of the information producer is to transfer the information to the broad audience via social web. Producer needs to identify interesting content and publish it to social web in the right time. Right timing of information publishing can increase the potential of spreading it. In the article we describe the process of interesting content identification and we present a model for right moment estimation of information publishing. Our estimation is based on producer’s web usage mining, web topic tracking and event identification.

Keywords

Social Web Web Content Mining Web Usage Mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shaparenko, B.: Information Genealogy: Uncovering the Flow of Ideas in Non-Hyperlinked Document Databases. In: International Conference on Knowledge Discovery and Data Mining, pp. 619–628. ACM, New York (2007)Google Scholar
  2. 2.
    Wang, X.: Mining Common Topics from Multiple Asynchronous Text Streams. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 182–201. ACM, NY (2009)Google Scholar
  3. 3.
    Krause, A.: Data Association for Topic Intensity Tracking. In: Proceedings of the 23rd international conference on Machine learning, pp. 497–504. ACM, NY (2006)CrossRefGoogle Scholar
  4. 4.
    Ma, H.: Mining Social Networks Using Heat Diffusion Processes for Marketing Candidates Selection. In: Proceeding of the 17th ACM conference on Information and knowledge management, pp. 233–242. ACM, NY (2008)CrossRefGoogle Scholar
  5. 5.
    Hartline, J.: Optimal Marketing Strategies over Social Networks. In: Proceeding of the 17th international conference on World Wide Web, pp. 189–198. ACM, NY (2008)CrossRefGoogle Scholar
  6. 6.
    Leskovec, J.: The Dynamics of Viral Marketing. In: Proceedings of the 7th ACM conference on Electronic commerce, pp. 228–257. ACM, NY (2007)Google Scholar
  7. 7.
    Richardson, M.: Mining Knowledge-Sharing Sites for Viral Marketing. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 61–70. ACM, New York (2002)CrossRefGoogle Scholar
  8. 8.
    Chung, S.: Dynamic Topic Mining from News Stream Data, pp. 653–670. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Tan, P., Kumar, V.: Web usage mining based on probabilistic latent semantic analysis. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 197–205. ACM, New York (2004)Google Scholar
  10. 10.
    Batista, P., Silva, M.J.: Mining Web Access Logs of an On-line Newspaper. In: Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (2002)Google Scholar
  11. 11.
    Chen, Y.: Advertising Keyword Suggestion Based on Concept Hierarchy. In: Proceedings of the international conference on Web search and web data mining, pp. 251–260. ACM, New York (2008)CrossRefGoogle Scholar
  12. 12.
    Xue, X.: Distributional Features for Text Categorization, pp. 428–442. IEEE, NJ (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tomas Kuzar
    • 1
  • Pavol Navrat
    • 1
  1. 1.Faculty of Informatics and Information TechnologiesSlovak University of Technology 

Personalised recommendations