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Extracting evolutionary communities in community question answering
Author(s) -
Zhang Zhongfeng,
Li Qiudan,
Zeng Daniel,
Gao Heng
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23003
Subject(s) - computer science , set (abstract data type) , quality (philosophy) , data science , variation (astronomy) , probabilistic logic , channel (broadcasting) , information retrieval , question answering , information extraction , world wide web , artificial intelligence , computer network , philosophy , physics , epistemology , astrophysics , programming language
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.

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