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Simplified detection and labeling of overlapping communities of interest in question-and-answer sites

Zide Meng 1 Fabien Gandon 1, 2 Catherine Faron Zucker 1, 2
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users’ communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as question-and-answer (Q&A) sites or forums, have no explicit social network structure. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose TTD (Topic Trees Distributions) an efficient approach for extracting topic from Q&A sites in order to detect communities of interest. Then we compare three detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. Our method based on topic modeling and user membership assignment is shown to be much simpler and faster while preserving the quality of the detection.
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Zide Meng, Fabien Gandon, Catherine Faron Zucker. Simplified detection and labeling of overlapping communities of interest in question-and-answer sites. IEEE/WIC/ACM 2015 - International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Dec 2015, Singapore, Singapore. pp.107-114, ⟨10.1109/WI-IAT.2015.184⟩. ⟨hal-01187400⟩



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