【摘要】 本文提出一种基于突发词聚类的跨媒体突发事件检测方法。根据事件分析,发现微博具有文本丰富、用户活跃度高、在突发事件检测中具有速度快且高效的特点,但是由于微博文本长度较短,内容过于随意,使得事件发现的结果不够精确。新闻作为官方媒体,其真实性和权威性较高,内容比较规范,事件发现较为准确,但因为新闻数量较少,对于突发事件检测任务来说,时效性较低。现有的方法只针对一种媒体的数据进行挖掘,无法规避掉该媒体的数据所固有的缺点。本文提出一种方法,将微博和新闻2种媒体的数据进行融合,在满足突发事件检测的时效性的同时,提升了突发事件检测的准确率。更多还原

【Abstract】 This paper proposes a cross-media bursty events detection method based on bursty words clustering.According to the events analysis,as Microblogs has a huge number of posts,users post or retweet Microblogs in anytime,it may spend fewer time detecting busty events than other platforms.However,many microblogs are advertisements and worthless,which leads to a lower precision.On the contrary,as an official media,news is highly authentic and authoritative,and contents of news are more standard.Therefore,events detection has a higher accuracy.However,due to the small number of news,the efficiency of busty events detection is low.At present,all of the existing detection methods only mine the data of one media,which face with a dilemma between efficiency and accuracy.In this paper,the proposed model fuses the data of two medias,microblog and newssin order to meet the needs of efficiency and improve the accuracy of emergency detection.更多还原