Editor's Summary The prolific commentary disseminated via Twitter on the riots in London and other British cities in August 2011 has given rise to the question of whether their reflection in such social media forums may have added to the unrest. Investigators analyzed 600,000 tweets and retweets about the riots for evidence that Twitter was used as a central organizational tool to promote illegal group action. Results indicated that irrelevant tweets died out and that Twitter users retweeted to show support for their beliefs in others' commentaries. Tweets offered by well‐known and popular individuals were more likely to be retweeted. In the case of the British riots, there is little overt evidence that Twitter was used to promote illegal activities at the time, though it was useful for spreading word about subsequent events.

No abstract

Healthcare professionals currently lack the means to gather unbiased and quantitative multi-modal data about the long-term behaviors of patients in their home environments. SPHERE is a multi-modal platform of non-medical sensors for behavior monitoring in residential environments that aims to overcome this major limitation of healthcare provision through using the inherently cost-efficient and scalable technologies of the Internet of Things (IoT). One of SPHERE's key tasks is to help to bring the next-generation low-power wireless networking and sensing technologies from the lab to the field by applying them in real-world environments. In this article we describe the highlights of SPHERE's system requirements, architecture, practical challenges, as well as of the design and deployment lessons learned. By leveraging novel IoT technologies such as the IEEE 802.15.4 TSCH network protocol, SPHERE has achieved successful initial deployments in twelve volunteer houses at the time of writing.

In this paper we explore a method of decomposition of compound tags found in social tagging systems and outline several results, including improvement of search indexes, extraction of semantic information, and benefits to usability. Analysis of tagging habits demonstrates that social tagging systems such as del.icio.us and flickr include both formal metadata, such as geotags, and informally created metadata, such as annotations and descriptions. The majority of tags represent informal metadata; that is, they are not structured according to a formal model, nor do they correspond to a formal ontology.Statistical exploration of the main tag corpus demonstrates that such searches use only a subset of the available tags; for example, many tags are composed as ad hoc compounds of terms. In order to improve accuracy of searching across the data contained within these tags, a method must be employed to decompose compounds in such a way that there is a high degree of confidence in the result. An approach to decomposition of English-language compounds, designed for use within a small initial sample tagset, is described. Possible decompositions are identified from a generous wordlist, subject to selective lexicon snipping. In order to identify the most likely, a Bayesian classifier is used across term elements. To compensate for the limited sample set, a word classifier is employed and the results classified using a similar method, resulting in a successful classification rate of 88%, and a false negative rate of only 1%.