Discovering collective viewpoints on micro-blogging events based on community and temporal aspects

  • Authors:

  • Bin Zhao;Zhao Zhang;Yanhui Gu;Xueqing Gong;Weining Qian;Aoying Zhou

  • Affiliations:

  • Institute of Massive Computing, East China Normal University, Shanghai, P.R. China;Institute of Massive Computing, East China Normal University, Shanghai, P.R. China;Dept. of Information and Communication Engineering, The University of Tokyo, Japan;Institute of Massive Computing, East China Normal University, Shanghai, P.R. China;Institute of Massive Computing, East China Normal University, Shanghai, P.R. China;Institute of Massive Computing, East China Normal University, Shanghai, P.R. China

  • Venue:
  • ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
  • Year:
  • 2011

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Abstract

Towards hot events, microblogs usually collect diverse and abundant thoughts, comments and opinions in a short period. It is interesting and meaningful to find how users are thinking about such events. In this paper, we aim to mine collective viewpoints from micro-blogging messages for any given event. Since a user can post multiple messages in a discussion, a user may have multiple viewpoints on a given event. Also user viewpoints may change under the influence of external events, such as news releases and activities, as time goes by. These present challenging of extracting collective viewpoints. To address this, we propose a T erm-Tw eet-U ser (TWU ) graph, which simultaneously incorporates text content, community structure and temporal information, to model user postings over time. We first identify representative terms from tweets, which constitute collective viewpoints. And then we apply Random Walk on TWU graph to measure the relevance between terms and group them into collective viewpoints. Finally, we evaluated our approach based on 817,422 tweets collected from Sina microblog, which is the biggest microblog in China. Experiments on the real dataset show the effectiveness of our model and algorithms.