Knowledge discovery from online communities

Luca Cagliero, Alessandro Fiori

Research output: Chapter in Book/Report/Conference proceedingChapter


During recent years, the outstanding growth of social network communities has caught the attention of the research community. A huge amount of user-generated content is shared among community users and gives researchers the unique opportunity to thoroughly investigate social community behavior. Many studies have been focused on both developing models to investigate user and collective behavior and building applications tailored to the most common community user activities. This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Original languageEnglish
Title of host publicationSocial Networking and Community Behavior Modeling: Qualitative and Quantitative Measures
PublisherIGI Global
Number of pages23
ISBN (Print)9781613504444
Publication statusPublished - 2011

ASJC Scopus subject areas

  • Computer Science(all)
  • Social Sciences(all)


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