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

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.

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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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