Collective Viewpoint Identification of Low-Level Participation

  • Bin Zhao
  • Zhao Zhang
  • Yanhui Gu
  • Weining Qian
  • Aoying Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7235)

Abstract

Mining microblogs is an important topic which can aid us to gather collective viewpoints on any event. However, user participation is low even for some hot events. Therefore, collective viewpoint discovery of low-level participation is a practical challenge. In this paper, we propose a Term-Retweet-Context (TRC) graph, which simultaneously incorporates text content and retweet context information, to model user retweeting. We first identify representative terms, which constitute collective viewpoints. And then we apply Random Walk on TRC graph to measure the relevance between terms and group them into collective viewpoints. Finally, extensive experiments conducted on real data collected from Sina microblog demonstrated that our proposal outperforms the state-of-the-art approaches.

Keywords

Short Text Graph Cluster User Participation Pointwise Mutual Information Random Walk Algorithm 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Bin Zhao
    • 1
    • 2
  • Zhao Zhang
    • 1
  • Yanhui Gu
    • 3
  • Weining Qian
    • 1
  • Aoying Zhou
    • 1
  1. 1.East China Normal UniversityChina
  2. 2.Nanjing Normal UniversityChina
  3. 3.The University of TokyoJapan

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