Tracking Idea Flows between Social Groups


  • Yangxin Zhong Tsinghua University
  • Shixia Liu Tsinghua University
  • Xiting Wang Tsinghua University
  • Jiannan Xiao Tsinghua University
  • Yangqiu Song West Virginia University



Idea flow, Information diffusion, Text mining, Temporal data, Social media


In many applications, ideas that are described by a set of words often flow between different groups. To facilitate users in analyzing the flow, we present a method to model the flow behaviors that aims at identifying the lead-lag relationships between word clusters of different user groups. In particular, an improved Bayesian conditional cointegration based on dynamic time warping is employed to learn links between words in different groups. A tensor-based technique is developed to cluster these linked words into different clusters (ideas) and track the flow of ideas. The main feature of the tensor representation is that we introduce two additional dimensions to represent both time and lead-lag relationships. Experiments on both synthetic and real datasets show that our method is more effective than methods based on traditional clustering techniques and achieves better accuracy. A case study was conducted to demonstrate the usefulness of our method in helping users understand the flow of ideas between different user groups on social media.




How to Cite

Zhong, Y., Liu, S., Wang, X., Xiao, J., & Song, Y. (2016). Tracking Idea Flows between Social Groups. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).



Technical Papers: Machine Learning Applications