Detecting topics and overlapping communities in Question and Answer sites

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, have no explicit social network structure. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an efficient approach for extracting topic from Q&A 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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Social Network Analysis and Mining, Springer, 2015, 5 (1), pp.27:1--27:17. <10.1007/s13278-015-0268-y>
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https://hal.archives-ouvertes.fr/hal-01187445
Contributeur : Catherine Faron Zucker <>
Soumis le : mercredi 13 juillet 2016 - 18:23:31
Dernière modification le : mardi 19 juillet 2016 - 01:05:06

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Zide Meng, Fabien Gandon, Catherine Faron-Zucker, Ge Song. Detecting topics and overlapping communities in Question and Answer sites. Social Network Analysis and Mining, Springer, 2015, 5 (1), pp.27:1--27:17. <10.1007/s13278-015-0268-y>. <hal-01187445>

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