• DocumentCode
    1467193
  • Title

    Toward Predicting Collective Behavior via Social Dimension Extraction

  • Author

    Tang, Lei ; Liu, Huan

  • Author_Institution
    Arizona State Univ., Tempe, AZ, USA
  • Volume
    25
  • Issue
    4
  • fYear
    2010
  • Firstpage
    19
  • Lastpage
    25
  • Abstract
    The SocioDim framework demonstrates promising results toward predicting collective behavior. However, many challenges require further research. For example, networks in social media are continually evolving, with new members joining a network and new connections established between existing members each day. This dynamic nature of networks entails efficient update of the model for collective behavior prediction. It is also intriguing to consider temporal fluctuation into the problem of collective behavior prediction.
  • Keywords
    behavioural sciences computing; social networking (online); SocioDim framework; collective behavior; social media; Advertising; Data mining; Facebook; Large-scale systems; MySpace; Social network services; Supervised learning; Twitter; Videos; YouTube; behavior prediction; collective behavior; edge-centric clustering; intelligent systems; node-centric clustering; social dimensions; social media;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2010.36
  • Filename
    5445065