• DocumentCode
    3734240
  • Title

    Predicting the integration of newcomers in OKBCs based on existing members´ involvement

  • Author

    Larise Lucia Stavarache;Mihai Dascalu;Stefan Trausan-Matu;Nicolae Nistor

  • Author_Institution
    Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Profiling online knowledge communities and determining their corresponding degree of newcomer integration based on existing members´ involvement, posts and comments helps us better understand what drives the social trend and how knowledge is built nowadays. In this study we differentiate participation from collaboration, thus showing how opinion leaders emerge in a community. Therefore, while analyzing 10 integrative and 10 non-integrative communities, we quantitatively measure member involvement in terms of previously validated automated indices that are used for assessing participation and collaboration. Afterwards, we build automated methods of classifying communities based on their members´ online behavior, thus being able to predict how likely new members will be integrated in the online community.
  • Keywords
    "Collaboration","Analytical models","Social network services","Sections","Semantics","Market research","Atmospheric measurements"
  • Publisher
    ieee
  • Conference_Titel
    Information, Intelligence, Systems and Applications (IISA), 2015 6th International Conference on
  • Type

    conf

  • DOI
    10.1109/IISA.2015.7388054
  • Filename
    7388054