TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter Mariam Adedoyin-Olowe 1 , Mohamed Medhat Gaber 1 , and Frederic Stahl 2 1 School of Computing, University of Portsmouth Hampshire, England, PO1 3HE, UK 2 School of Systems Engineering, University of Reading PO Box 225, Whiteknights, Reading, RG6 6AY, UK Abstract. The Twitter network has been labeled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credi- ble 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 asso- ciated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that de- scribe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of tempo- ral 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. Keywords: Twitter, Hashtags, Association Rule Mining, Association Rules, New Rules, Emerging Rules, Unexpected Rules, ’Dead’ Rules, Rule Matching 1 Introduction The surge in the acceptability of Twitter since its launch in 2007 has made it the most commonly used microblogging application [8], [16] (in this paper we used Twitter, Twitter network and the network interchangeably). The network permits the effective collection of large data which gives rise to major compu- tational challenges. More people are becoming interested in and are relying on Twitter for information and news on diverse topics. Twitter is mainly known for short instant messaging that allows a maximum of 140 characters per message (tweet). Users follow other users’ comments or contributions on events taking place globally in real time [5]. The network is labelled the most commonly used