计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 5-9.

• 智能计算 • 上一篇    下一篇

基于移动群智数据的城市热点事件感知方法

张佳凡,郭斌,路新江,於志文,周兴社   

  1. 西北工业大学计算机学院 西安710129,西北工业大学计算机学院 西安710129,西北工业大学计算机学院 西安710129,西北工业大学计算机学院 西安710129,西北工业大学计算机学院 西安710129
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家重点基础研究发展计划(973计划)(2015CB352400),国家自然科学基金(61332005,61373119,9)资助

Approach for Urban Popular Event Detection Using Mobile Crowdsourced Data

ZHANG Jia-fan, GUO Bin, LU Xin-jiang, YU Zhi-wen and ZHOU Xing-she   

  • Online:2018-11-14 Published:2018-11-14

摘要: 以新浪微博为研究对象,研究了基于移动群智数据的城市热点事件感知方法,对热点事件进行发现与分类。面向不同的应用需求,可将发现的热点事件分为物理事件与虚拟事件两大类。采用的方法首先根据热词的词频变化特征对新浪微博中的热词进行有效挖掘,然后根据热词的上下文语境进行层次聚类以得到热点事件描述。此外,通过分析信息量特征、时序特征及原创微博数目特征,采用不同方法进行事件分类。实验结果表明,不同的分类方法均可达到较高的准确率。

Abstract: This paper proposed an urban popular event detection and classification approach using the crowdsourced data from Sina Weibo.The detected events can be categorized into physical events or virtual events,which can be used for different applications.Our approach firstly extracts the hot words from crowd posts according to the characteristic of word frequency.With the context of hot words,hierarchical clustering is then used to obtain the description of popular events.By analyzing the three proposed features,including lexical entropy,temporal dynamics,and content originality,we applied various methods to do event classification.The experiment results indicate that all different classification methods can achieve a higher precision under our approach.

Key words: Microblogging,Popular event detection,Microblogging event classification,Mobile crowd sensing

[1] 刘云浩.群智感知计算[J].中国计算机学会通讯,2012,8(10):38-41
[2] Guo B,Yu Z,Zhou X,et al.From Participatory Sensing to Mobile Crowd Sensing[C]∥2014 IEEE International Conference on Pervasive Computing and Communications Workshops(PERCOM Workshops).IEEE,2014:593-598
[3] Cui A,Zhang M,Liu Y,et al.Discover breaking events withpopular hashtags in twitter[C]∥Proceedings of the 21st ACM International Conference on Information and Knowledge Mana-gement.ACM,2012:1794-1798
[4] Ozdikis O,Senkul P,Oguztuzun H.Semantic expansion of hashtags for enhanced event detection in Twitter[C]∥Proceedings of the 1st International Workshop on Online Social Systems.2012
[5] Mathioudakis M,Koudas N.Twittermonitor:trend detectionover the twitter stream[C]∥Proceedings of the 2010 ACM SIGMOD International Conference on Management of data.ACM,2010:1155-1158
[6] Gupta M,Gao J,Zhai C X,et al.Predicting future popularitytrend of events in microblogging platforms[J].Proceedings of the American Society for Information Science and Technology,2012,49(1):1-10
[7] 郑斐然,苗夺谦,张志飞.一种中文微博新闻话题检测的方法[J].计算机科学,2012,39(1):138-141
[8] 郭跇秀,吕学强,李卓.基于突发词聚类的微博突发事件检测方法[J].计算机应用,2014,34(2):486-490
[9] Zhang J,Liu B,Tang J,et al.Social influence locality for mode-ling retweeting behaviors[C]∥Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.AAAI Press,2013:2761-2767
[10] Zhao S,Zhong L,Wickramasuriya J,et al.Human as real-time sensors of social and physical events:A case study of twitter and sports games[J].arXiv preprint arXiv:1106.4300,2011
[11] Lee R,Wakamiya S,Sumiya K.Discovery of unusual regionalsocial activities using geo-tagged microblogs[J].World Wide Web,2011,14(4):321-349
[12] Weiler A,Scholl M H,Wanner F,et al.Event identification for local areas using social media streaming data[C]∥Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks.ACM,2013:1-6
[13] Boettcher A,Lee D.EventRadar:A real-time local event detection scheme using twitter stream[C]∥2012 IEEE International Conference on Green Computing and Communications(GreenCom).IEEE,2012:358-367
[14] Barabasi A L.The origin of bursts and heavy tails in human dynamics[J].Nature,2005,435(7039):207-211
[15] Leskovec J,McGlohon M,Faloutsos C,et al.Patterns of Cascading behavior in large blog graphs[C]∥SDM.2007,7:551-556

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