• DocumentCode
    1692241
  • Title

    Analyzing group dynamics for incidental topics in online social networks

  • Author

    Zhou, Yadong ; Guan, Xiaohong ; Zheng, Qinghua ; Sun, Qindong ; Zhao, Junzhou

  • Author_Institution
    MOE KLINNS Lab., Xi´´an Jiaotong Univ., Xi´´an, China
  • fYear
    2010
  • Firstpage
    1941
  • Lastpage
    1946
  • Abstract
    Groups discussing popular topics in online social networks are of great interests recently. In this paper, we measure the dynamics of the online groups discussing incidental popular topics and present method for predicting the dynamic sizes of incidental topic groups. It is found that the dynamic sizes of incidental topic groups follow the law of heavy-tail. Based on the heavy-tailed theory a prediction method is developed for analyzing the dynamics of this type of groups. The models and methods developed in the paper are validated using the actual data from SOHU blog sites, one of the most influential blog sites in China. The experiment results show that the method can predict the dynamic size of incidental topic groups with both short and long time scales.
  • Keywords
    Internet; groupware; peer-to-peer computing; social networking (online); social sciences computing; statistics; China; SOHU blog site; group dynamics; heavy tail law; incidental topic; information propagation; online social network; prediction method; topic tracking; Information services; Internet; Mathematical model; Predictive models; Social network services; Web sites; Heavy-tail; Information propagation; Online social networks; Topic group dynamics; Topic tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5554627
  • Filename
    5554627