Accessibility navigation

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

Adedoyin-Olowe, M., Gaber, M. M. and Stahl, F. ORCID: (2013) TRCM: a methodology for temporal analysis of evolving concepts in Twitter. Lecture Notes in Computer Science, 7895. pp. 135-145. ISSN 0302-9743 (Proceedings, Part II. 12th International Conference on Artificial Intelligence and Soft Computing)

Text - Accepted Version
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1007/978-3-642-38610-7_13


The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:32163
Additional Information:Paper presented at the 12th International Conference on Artificial Intelligence and Soft Computing ICAISC 2013, Zakopane, Poland, June 9-13, 2013


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation