Discovering burst patterns of burst topic in Twitter

• Main Authors: DONG, Guozhong, YANG, Wu, ZHU, Feida, WANG, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Burst pattern; Burst topic; Frequent sub-graph mining; Hierarchical clustering; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/3598; https://ink.library.smu.edu.sg/context/sis_research/article/4599/viewcontent/Discovering_burst_patterns_of_burst_topic_in_twitter.pdf
• Institution: Singapore Management University

Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. A burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro perspective. Frequent sub-graph mining is used to discover the information flow patterns of burst topic from the micro perspective. Experimental results show that several interesting burst patterns are discovered, which can reveal different burst topic clusters and frequent information flows of burst topic.