An Efficient Online Event Detection Method for Microblogs via User Modeling

摘要：

　　Detecting events in microblog is important but still challenging.As tweet stream is a mixture of user interests and external events,its expensive to distinguish them.Existing methods are ineffective since they ignore user interests or only model interests and events on a fixed dataset without scalability.In this paper,we introduce an online learning model User Modeling Based Interest and Event Topic Model (UMIETM).UMIETM (1) exploits user modelings information to discover events,which usually capture attentions from users with different interests,and (2) treats the arriving data as stream and run the detection in online learning style.Furthermore,UMIETM can handle dynamic increased vocabulary in tweet stream.The UMIETM is verified on the real dataset which spans one year and contains 16 million tweets,and it outperforms state-of-the-art models in quantitative.

关键词： Event detection Online learning model Microblog stream User modeling

作者: Weijing Huang Wei Chen Lamei Zhang Weijing Huang

作者单位: Key Laboratory of High Confidence Software Technologies(Ministry of Education),EECS,Peking Universit Baidu Inc.,Beijing,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 329-341

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）