NDLI: Using dynamic community detection to identify trends in user-generated content

Content Provider	Springer Nature Link
Author	Cazabet, Rémy Takeda, Hideaki Hamasaki, Masahiro Amblard, Frédéric
Copyright Year	2012
Abstract	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.
Starting Page	361
Ending Page	371
Page Count	11
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	2
Issue Number	4
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2012-06-21
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Methodology of the Social Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Complex Networks Game Theory, Economics, Social and Behav. Sciences Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology

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