an article about the usage of twitter with the result, that 90% of all tweets were produced by just 10% of all twitter-users. there was also some research about the gender's influence on twitter usage
Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.
Mark Gibbs ponders how to analyze Twitter for a specific search term using the Twitter search API and Microsoft Excel.
Dazu die Links zu den weiteren Teilen 2-4.
Following up on KMeans Clustering Now Running on Elastic MapReduce, Stephen Green has generously documented the steps that was necessary to get an example of k-Means clustering up and running on Amazon’s Elastic MapReduce (EMR) on the Apache Lucene Mahout wiki.
M. zur Muehlen, M. Indulska, and K. Kittel. 19th Australasian Conference on Information Systems (ACIS 2008), Christchurch, New Zealand, Australasian Computer Society, (2008)
T. Nguyen, J. Schiefer, and A. Tjoa. DOLAP '05: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, page 77--86. New York, NY, USA, ACM, (2005)