Social Network Analysis and Mining

, Volume 2, Issue 4, pp 361–371 | Cite as

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

  • Rémy Cazabet
  • Hideaki Takeda
  • Masahiro Hamasaki
  • Frédéric Amblard
Original Article


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.


Social Network Video Game Dynamic Network Community Detection Trend Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Rémy Cazabet
    • 1
  • Hideaki Takeda
    • 2
  • Masahiro Hamasaki
    • 3
  • Frédéric Amblard
    • 4
  1. 1.IRITUniversity of ToulouseToulouseFrance
  2. 2.National Institute of Informatics (NII)TokyoJapan
  3. 3.National Institute of AISTJST CRESTTokyoJapan
  4. 4.IRIT, UT1University of Social ScienceToulouseFrance

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