Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events

被引:5
作者
Adedoyin-Olowe, Mariam [1 ]
Gaber, Mohamed Medhat [1 ]
Dancausa, Carlos Martin [1 ]
Stahl, Frederic [2 ]
机构
[1] Robert Gordon Univ, Sch Comp Sci & Digital Media, Aberdeen AB10 7GJ, Scotland
[2] Univ Reading, Sch Syst Engn, Reading RG6 6AY, Berks, England
来源
2014 13TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2014年
关键词
D O I
10.1109/ICMLA.2014.38
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hashtag keywords of tweets in different time-slots. Our system identify evolving hashtag keywords with strong association rules in each time-slot. We then map the identified hashtag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.
引用
收藏
页码:207 / 212
页数:6
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