Abstract
This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.
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References
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© 2013 Springer International Publishing Switzerland
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Gomes, J.B., Adedoyin-Olowe, M., Gaber, M.M., Stahl, F. (2013). Rule Type Identification Using TRCM for Trend Analysis in Twitter. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_20
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DOI: https://doi.org/10.1007/978-3-319-02621-3_20
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Online ISBN: 978-3-319-02621-3
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