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Rule Type Identification using TRCM for trend analysis in Twitter

Gomes, J., Adedoyin-Olowe, M., Gaber, M. and Stahl, F. (2013) Rule Type Identification using TRCM for trend analysis in Twitter. In: Thirty-Third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 10-12 DECEMBER 2013, Cambridge UK, pp. 273-278.

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Official URL: http://dx.doi.org/10.1007/978-3-319-02621-3_20

Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36412
Additional Information:ISBN: 9783319026213
Publisher:Springer International Publishing

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