Extraction of unexpected rules from Twitter hashtags and its application to sport events

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C. M. and Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 (2014) Extraction of unexpected rules from Twitter hashtags and its application to sport events. In: 13th International Conference on Machine Learning and Applications (ICMLA 2014), 3-5 Dec 2014, Detriot, MI, USA, pp. 207-212.

Abstract/Summary

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 hash tag keywords of tweets in different time-slots. Our system identify evolving hash tag keywords with strong association rules in each time-slot. We then map the identified hash tag 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.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/40751
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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