Analyzing community knowledge sharing behavior

Styliani Kleanthous, Vania Dimitrova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

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
Pages231-242
Number of pages12
Volume6075 LNCS
DOIs
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

Other

Other18th International Conference on User Modeling, Adaptation and Personalization, UMAP 2010
CountryUnited States
CityBig Island, HI
Period20/06/1024/06/10

Keywords

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

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  • Cite this

    Kleanthous, S., & Dimitrova, V. (2010). Analyzing community knowledge sharing behavior. In User Modeling, Adaptation, and Personalization - 18th International Conference, UMAP 2010, Proceedings (Vol. 6075 LNCS, pp. 231-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6075 LNCS). https://doi.org/10.1007/978-3-642-13470-8_22