Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC HUS worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.
Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62 and a recall of 75 evaluated using manually annotated, real-world data.  Event-Based Epidemic Intelligence (e-EI) has arisen as a body of work which relies upon different forms of pattern recognition in order to detect the disease reporting events from unstructured text that is present on the Web. Current supervised approaches to e-EI suffer both from high initial and high maintenance costs, due to the need to manually label examples to train and update a classifier for detecting disease reporting events in dynamic information sources, such as blogs. In this paper, we propose a new method for the supervised detection of disease reporting events. We tackle the burden of manually labelling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically classify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then transferred to the task of classifying blogs. Our experiments show that with the automatic classification of training data, and the transfer approach, we achieve an overall precision of 92 and an accuracy of 78.20 .