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

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.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Vox Populi, Vox Dei? A closer look at the ‘public opinion’ argument for retention

The Japanese government’s justification for retaining the death penalty is that abolition would erode the legitimacy of and public trust in the criminal justice system, leading to victims’ families taking justice into their own hands. This justification is based on the results of a regularly administered public opinion survey, which is said to show strong public support for the death penalty. However, a close analysis of the results of the 2014 survey fails to validate this claim. Just over a third of respondents were committed to retaining the death penalty at all costs, while the rest accepted the possibility of future abolition, with some of them seeing this as contingent on the introduction of life imprisonment without parole as an alternative sentence. These findings hardly describe a society that expects the strict application of the death penalty and whose trust in justice depends on the government’s commitment to retaining it. My reading of the 2014 survey is that the Japanese public is ready to embrace abolition. Japan, after all, is a signatory to the International Covenant on Civil and Political Rights, which calls on states not to delay or prevent abolition, so this should be welcome news for the Japanese government!