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

Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Carlos Dancausa, Frederic Stahl

    Research output: Chapter in Book/Report/Conference proceedingChapter


    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.
    Original languageEnglish
    Title of host publicationProceedings of 13th International Conference on Machine Learning and Applications
    Number of pages6
    Publication statusPublished (VoR) - 2014


    • Twitter
    • Association rules
    • Games
    • Fans
    • Pattern matching
    • Educational institutions


    Dive into the research topics of 'Extraction of unexpected rules from Twitter hashtags and its application to sports events'. Together they form a unique fingerprint.

    Cite this