The increase of interest in using social media as a source for research has motivated tackling the challenge of automatically geolocating tweets, given the lack of explicit location information in the majority of tweets. In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyze the extent to which a tweet's country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyze the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone-the most widely used feature in previous work-leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20 and 50 percent. We observe that tweet content, the user's self-reported location and the user's real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.

Agent-based simulation models provide a way to investigate social phenomena that complement existing social science methods. Recent advances in computing hardware such as the wide availability of multi-core CPUs and increased main memory capacities make it possible to investigate population-scale phenomena using commodity compute resources. This paper describes experiences made in the development of an example model that utilises multiple CPU cores and an investigation of the scalability of the resulting code. We argue that commodity compute resources and commoditised simulation frameworks can now be used to simulate real-world populations and to use these simulations to investigate social phenomena such as migration.

This paper explores what it takes to develop digital objects (software but also other products such as workflows or data collections), which are both sustainable and achieve demonstrable uptake and usage. We focus in particular on objects produced in the context of e-Research and on the specific conditions this context brings with it.

The EUAsiaGrid project has been funded by the European Commission to widen the uptake of e-research and, more specifically, the EGEE e-Infrastructure in the Asia-Pacific region. The consortium provides support for infrastructure development and application porting as well as training, education and outreach. We describe the background of the project as well as its structure and workplan.

This paper reports findings from an ethnographic study of the work of Adult and Care of the Elderly Community Mental Health Teams in the context of the deployment of an Electronic Medical Record. Our findings highlight the importance of informal discussions and provisional judgments as part of the process by which teams achieve consensual clinical management decisions over time. We show how paper-based documentation supports this collaborative work by affording both the revision of preliminary clinical management options and the accretion of contributions by team members with different clinical perspectives and expertise. Finally, we consider the implications both for teamwork and the Integrated Care Record (ICR) as clinical documentation becomes increasingly held and distributed electronically.