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Using dynamic community detection to identify trends in user-generated content
In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network o...
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Published in: | Social network analysis and mining 2012-12, Vol.2 (4), p.361-371 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | In this paper, we present a new solution for trend detection in user-generated content, and more particularly Web 2.0 social networks. Whereas some propositions have been published in this domain recently, we have chosen a new approach based on network analysis. We first create an evolving network of terms, which is an abstraction of the complete network, and then run a dynamic community detection algorithm on this evolving network. In order to be able to detect not only short, bursting events, but also more persistent topics, we test our solution on a social network for which we have information about all published contents for a period of more than 2 years: the Japanese network Nico Nico Douga. After presenting our solution in detail, we present the results on this dataset, notably a statistical analysis of communities’ sizes and durations, examples of detected communities, and a typology of the different kinds of trends detected. Finally, we discuss the advantages and disadvantages of this method, as well as its possible applications. |
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ISSN: | 1869-5450 1869-5469 |
DOI: | 10.1007/s13278-012-0074-8 |