TDT_CC: A Hot Topic Detection and Tracking Algorithm Based on Chain of Causes

  • Zhen Hong Liu
  • Gong Liang Hu
  • Tie Hua Zhou
  • Ling Wang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)


With the development and application of Web3.0, it has become a common social phenomenon that users discuss hot topics on social networks, making them to aggregate into user groups based on the topics, rapidly. The hot topic detection and tracking is helpful for social public opinion supervision and guidance, in addition, it contribute to the user’s behavior mining and analysis. However, users’ interest in some topics often changes as new event occurs, causing the center of hot topics to change over time. For tracking the heat of topic in real-time, we proposed an effective algorithm to detect and track hot topic based on chain of causes (TDT_CC). Firstly, we treat the events as attributes of topic and add them to the structure of the social networks. Secondly, the subgraphs that induced by specific attributes are mined based on the correlation of event-heat-changing attributes and attribute-extended social network structure.


Hot topic Social networks Chain of causes Attributes correlation Subgraph mining 



This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science Research of Education Department of Jilin Province (No. JJKH20180422KJ).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhen Hong Liu
    • 1
  • Gong Liang Hu
    • 1
  • Tie Hua Zhou
    • 1
  • Ling Wang
    • 1
  1. 1.Department of Computer Science and Technology, School of Computer ScienceNortheast Electric Power UniversityJilinChina

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