Extracting evolutionary communities in community question answering

Zhongfeng Zhang, Qiudan Li, Daniel Zeng, Heng Gao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.

Original languageEnglish (US)
Pages (from-to)1170-1186
Number of pages17
JournalJournal of the Association for Information Science and Technology
Issue number6
StatePublished - Jun 2014

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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