作者: Yu-Ru LinK. Selçcuk CandanHari SundaramLexing Xie
刊名: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 2011, Vol.7S (1), pp.1-22
来源数据库: Association for Computing Machinery Journal
DOI: 10.1145/2037676.2037686
关键词: Social mediamultirelational learningsocial network analysisstream miningtensor analysis
原始语种摘要: We propose SCENT, an innovative, scalable spectral analysis framework for internet scale monitoring of multirelational social media data, encoded in the form of tensor streams. In particular, a significant challenge is to detect key changes in the social media data, which could reflect important events in the real world, sufficiently quickly. Social media data have three challenging characteristics. First, data sizes are enormous; recent technological advances allow hundreds of millions of users to create and share content within online social networks. Second, social data are often multifaceted (i.e., have many dimensions of potential interest, from the textual content to user metadata). Finally, the data is dynamic; structural changes can occur at multiple time scales and be localized...
全文获取路径: ACM  (合作)

  • social 群居的
  • tensor 张量
  • particular 细致的
  • information 报告
  • extend 延长
  • dynamic 动力学的
  • faster 加快
  • share 分配
  • three 
  • dimensions 面积