[1]王冰玉,吴振宇,沈苏彬,等.社交媒体事件检测研究综述[J].计算机技术与发展,2018,28(09):105-111.[doi:10．3969/j．issn．1673－629X．2018．09．022]
　WANG Bingyu,WU Zhenyu,SHEN Subin,et al.Survey on Event Detection Ｒesearch in Social Media[J].,2018,28(09):105-111.[doi:10．3969/j．issn．1673－629X．2018．09．022]
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社交媒体事件检测研究综述()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年09期
页码:
105-111
栏目:
智能、算法、系统工程
出版日期:
2018-09-10

文章信息/Info

Title:
Survey on Event Detection Ｒesearch in Social Media
文章编号:
1673－629X(2018)09－0105－07
作者:
王冰玉1 ; 吴振宇1; 沈苏彬2; 陈佳颖1
1．南京邮电大学 物联网学院，江苏 南京 210000; 2．南京邮电大学 计算机学院，江苏 南京 210000
Author(s):
WANG Bing－yu1; WU Zhen－yu1; SHEN Su－bin2; CHEN Jia－ying1
1．School of Internet of Things，Nanjing University of Posts and Telecommunications，Nanjing 210000，China; 2．School of Computer Science ＆ Technology，Nanjing University of Posts and Telecommunications，Nanjing 210000，China
关键词:
事件检测; 事件; 话题; 社交媒体
Keywords:
event detection; event; topic; social media
分类号:
TP391
DOI:
10．3969/j．issn．1673－629X．2018．09．022
文献标志码:
A
摘要:
事件检测是社交媒体挖掘的重要内容之一。目前，已经提出多种针对社交媒体数据的事件检测方法。然而，对事件的定义以及检测方法的优缺点尚未明确说明。因此，首先对事件的定义进行了说明，分析了事件与话题等易混淆概念之间的区别与联系，事件相对于话题更具局限性，而同一话题下可能涵盖多个相似或相关事件。其次，从社交媒体数据类型的角度出发，分析和总结了社交媒体事件检测方法的优缺点以及适用场景，传统媒体中常使用的基于 Single－Pass、基于突发项等原理及实现简单，但是适用场景具有局限性，基于聚类的方法可实现无监督的事件自动检测，但其大部分实现都相对复杂。基于社交数据的方式则可以利用用户行为信息更及时地发现热点事件。最后，对事件检测的未来发展方向进行了展望。
Abstract:
Event detection is an important part in social media data mining field． At present，a variety of methods of event detection to social media data are put forward． However，the definition of event and the advantages and disadvantages of exist methods have not been clearly stated． Therefore，we firstly describe the definition of event and analyze the differences and connections between confusing concepts such as events and topics． In general，the concept of event is narrow than topic as the same topic may cover multiple similar or related events． Secondly，from the point of data type，the merits and demerits of event detection methods and the application scenarios are discussed and summarized． Methods based on Single－Pass and burst items are simple and easily implemented while having the limitations of application scenario． Clustering－based methods can detect events without supervision but are relatively complex and time－costing． Methods aiming at social user data is a new direction deserve to deep research as they utilize user behavior information in time which can detect events more timely． Finally，the future direction of event detection is prospected．

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更新日期/Last Update: 2018-09-10