Online burst events detection oriented real-time microblog message stream

• Main Authors: DONG, Guozhong, GAO, Jun, HUANG, Liang, SHI, Chunlei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Burst event; abnormal message; microblog; message stream; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/10099; https://ink.library.smu.edu.sg/context/sis_research/article/11098/viewcontent/TSP_CMC_5601.pdf
• Institution: Singapore Management University

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. Combined with event features, an online incremental clustering algorithm is used to cluster abnormal messages and detect burst events. Experimental results in the real-time microblog message stream environment show that our framework can be used in online burst events detection and has higher accuracy compared with other approaches.