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.

Are investors guided by the news disclosed by companies or by journalists?

Most previous studies demonstrating the influential role of the textual information released by the media on stock market performance have concentrated on earnings-related disclosures. By contrast, this paper focuses on disposal announcements, so that the impacts of listed companies’ announcements and journalists’ stories can be compared concerning the same events. Consistent with previous findings, negative words, rather than those expressing other types of sentiment, statistically significantly affect adjusted returns and detrended trading volumes. However, extending previous studies, the results of this paper indicate that shareholders’ decisions are mainly guided by the negative sentiment in listed companies’ announcements rather than that in journalists’ stories. Furthermore, this effect is restricted to the announcement day. The average market reaction–measured by adjusted returns–is inversely related only when the announcements are ignored by the media, but the dispersion of market reaction–measured by detrended trading volume–is positively affected only when announcements are followed up by journalists.

How Islamic is al-Qaeda? The politics of Pan-Islam and the challenge of modernisation

This article investigates the contested ideology of al-Qaeda through an analysis of Osama bin Ladin’s writings and public statements issued between 1994 and 2011, set in relation to the development of Islamic thought and changing socio-political realities in the late nineteenth and twentieth centuries. Challenging popular conceptions of Wahhabism and the “Salafi jihad”, it reveals an idealistic, Pan-Islamic sentiment at the core of his messages that is not based on the main schools of Islamic theology, but is the result of a crisis of meaning of Islam in the modern world. Both before and after the death of al-Qaeda’s iconic leader, the continuing process of religious, political and intellectual fragmentation of the Muslim world has led to bin Ladin’s vision for unity being replaced by local factions and individuals pursuing their own agendas in the name of al-Qaeda and Islam.

In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.