Analyzing community knowledge sharing behavior

Styliani Kleanthous, Vania Dimitrova

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    8 Citations (Scopus)


    The effectiveness of personalized support provided to virtual communities depends on what we know about a particular community and in which areas the community may need support. Following organizational psychology theories, we have developed algorithms to automatically detect patterns of knowledge sharing in a closely-knit virtual community, focusing on transactive memory, shared mental models, and cognitive centrality. The automatic detection of problematic areas enables taking decisions about notifications targeted at different community members but aiming at improving the functioning of the community as a whole. The paper presents graph-based algorithms for detecting community knowledge sharing patterns, and illustrates, based on a study with an existing community, how these patterns can be used for community-tailored support.

    Original languageEnglish
    Title of host publicationUser Modeling, Adaptation, and Personalization - 18th International Conference, UMAP 2010, Proceedings
    Number of pages12
    Volume6075 LNCS
    Publication statusPublished - 2010
    Event18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010 - Big Island, HI, United States
    Duration: 20 Jun 201024 Jun 2010

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume6075 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010
    Country/TerritoryUnited States
    CityBig Island, HI


    • Closely-knit Communities
    • Community Knowledge Sharing
    • Graph Mining for Community Modelling


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